Package Summary
Tags | No category tags. |
Version | 0.44.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/autowarefoundation/autoware_universe.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-09-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kenzo Lobos-Tsunekawa
- Amadeusz Szymko
Authors
autoware_ptv3
Purpose
The autoware_ptv3
package is used for 3D lidar segmentation.
Inner-workings / Algorithms
This package implements a TensorRT powered inference node for Point Transformers V3 (PTv3) [1]. The sparse convolution backend corresponds to spconv. Autoware installs it automatically in its setup script. If needed, the user can also build it and install it following the following instructions.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/pointcloud |
sensor_msgs::msg::PointCloud2 |
Input pointcloud topic. |
Output
Name | Type | Description |
---|---|---|
~/output/segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
RGB segmented pointcloud. |
~/output/ground_segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
Pointcloud with the ground segmented out. |
~/output/probs/pointcloud |
sensor_msgs::msg::PointCloud2 |
Class probabilities segmented pointcloud. |
debug/cyclic_time_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Cyclic time (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
PTv3Node node
{{ json_to_markdown(“perception/autoware_ptv3/schema/ptv3.schema.json”) }}
PTv3Node model
{{ json_to_markdown(“perception/autoware_ptv3/schema/ml_package_ptv3.schema.json”) }}
The build_only
option
The autoware_ptv3
node has a build_only
option to build the TensorRT engine file from the specified ONNX file, after which the program exits.
ros2 launch autoware_ptv3 ptv3.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_ptv3
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_ptv3 ptv3.launch.xml log_level:=debug
Assumptions / Known limits
This node assumes that the input pointcloud follows the PointXYZIRC
layout defined in autoware_point_types
.
Trained Models
- v1 – First model release trained with PoC pseudo labels for the internal T4 dataset.
Changelog
References/External links
[1] Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. “Point Transformer V3: Simpler, Faster, Stronger.” 2024 Conference on Computer Vision and Pattern Recognition.
Package Dependencies
System Dependencies
Dependant Packages
Launch files
- launch/ptv3.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- segmented/pointcloud [default: /sensing/lidar/concatenated/segmented]
- ground_segmented/pointcloud [default: /sensing/lidar/concatenated/no_ground]
- probs/pointcloud [default: /sensing/lidar/concatenated/probs]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: ptv3]
- model_path [default: $(var data_path)/ptv3]
- model_param_path [default: $(find-pkg-share autoware_ptv3)/config/$(var model_name).param.yaml]
- ml_package_param_path [default: $(var model_path)/ml_package_$(var model_name).param.yaml]
- class_remapper_param_path [default: $(var model_path)/detection_class_remapper.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_ptv3 at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.44.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/autowarefoundation/autoware_universe.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-09-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kenzo Lobos-Tsunekawa
- Amadeusz Szymko
Authors
autoware_ptv3
Purpose
The autoware_ptv3
package is used for 3D lidar segmentation.
Inner-workings / Algorithms
This package implements a TensorRT powered inference node for Point Transformers V3 (PTv3) [1]. The sparse convolution backend corresponds to spconv. Autoware installs it automatically in its setup script. If needed, the user can also build it and install it following the following instructions.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/pointcloud |
sensor_msgs::msg::PointCloud2 |
Input pointcloud topic. |
Output
Name | Type | Description |
---|---|---|
~/output/segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
RGB segmented pointcloud. |
~/output/ground_segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
Pointcloud with the ground segmented out. |
~/output/probs/pointcloud |
sensor_msgs::msg::PointCloud2 |
Class probabilities segmented pointcloud. |
debug/cyclic_time_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Cyclic time (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
PTv3Node node
{{ json_to_markdown(“perception/autoware_ptv3/schema/ptv3.schema.json”) }}
PTv3Node model
{{ json_to_markdown(“perception/autoware_ptv3/schema/ml_package_ptv3.schema.json”) }}
The build_only
option
The autoware_ptv3
node has a build_only
option to build the TensorRT engine file from the specified ONNX file, after which the program exits.
ros2 launch autoware_ptv3 ptv3.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_ptv3
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_ptv3 ptv3.launch.xml log_level:=debug
Assumptions / Known limits
This node assumes that the input pointcloud follows the PointXYZIRC
layout defined in autoware_point_types
.
Trained Models
- v1 – First model release trained with PoC pseudo labels for the internal T4 dataset.
Changelog
References/External links
[1] Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. “Point Transformer V3: Simpler, Faster, Stronger.” 2024 Conference on Computer Vision and Pattern Recognition.
Package Dependencies
System Dependencies
Dependant Packages
Launch files
- launch/ptv3.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- segmented/pointcloud [default: /sensing/lidar/concatenated/segmented]
- ground_segmented/pointcloud [default: /sensing/lidar/concatenated/no_ground]
- probs/pointcloud [default: /sensing/lidar/concatenated/probs]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: ptv3]
- model_path [default: $(var data_path)/ptv3]
- model_param_path [default: $(find-pkg-share autoware_ptv3)/config/$(var model_name).param.yaml]
- ml_package_param_path [default: $(var model_path)/ml_package_$(var model_name).param.yaml]
- class_remapper_param_path [default: $(var model_path)/detection_class_remapper.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_ptv3 at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.44.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/autowarefoundation/autoware_universe.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-09-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kenzo Lobos-Tsunekawa
- Amadeusz Szymko
Authors
autoware_ptv3
Purpose
The autoware_ptv3
package is used for 3D lidar segmentation.
Inner-workings / Algorithms
This package implements a TensorRT powered inference node for Point Transformers V3 (PTv3) [1]. The sparse convolution backend corresponds to spconv. Autoware installs it automatically in its setup script. If needed, the user can also build it and install it following the following instructions.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/pointcloud |
sensor_msgs::msg::PointCloud2 |
Input pointcloud topic. |
Output
Name | Type | Description |
---|---|---|
~/output/segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
RGB segmented pointcloud. |
~/output/ground_segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
Pointcloud with the ground segmented out. |
~/output/probs/pointcloud |
sensor_msgs::msg::PointCloud2 |
Class probabilities segmented pointcloud. |
debug/cyclic_time_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Cyclic time (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
PTv3Node node
{{ json_to_markdown(“perception/autoware_ptv3/schema/ptv3.schema.json”) }}
PTv3Node model
{{ json_to_markdown(“perception/autoware_ptv3/schema/ml_package_ptv3.schema.json”) }}
The build_only
option
The autoware_ptv3
node has a build_only
option to build the TensorRT engine file from the specified ONNX file, after which the program exits.
ros2 launch autoware_ptv3 ptv3.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_ptv3
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_ptv3 ptv3.launch.xml log_level:=debug
Assumptions / Known limits
This node assumes that the input pointcloud follows the PointXYZIRC
layout defined in autoware_point_types
.
Trained Models
- v1 – First model release trained with PoC pseudo labels for the internal T4 dataset.
Changelog
References/External links
[1] Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. “Point Transformer V3: Simpler, Faster, Stronger.” 2024 Conference on Computer Vision and Pattern Recognition.
Package Dependencies
System Dependencies
Dependant Packages
Launch files
- launch/ptv3.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- segmented/pointcloud [default: /sensing/lidar/concatenated/segmented]
- ground_segmented/pointcloud [default: /sensing/lidar/concatenated/no_ground]
- probs/pointcloud [default: /sensing/lidar/concatenated/probs]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: ptv3]
- model_path [default: $(var data_path)/ptv3]
- model_param_path [default: $(find-pkg-share autoware_ptv3)/config/$(var model_name).param.yaml]
- ml_package_param_path [default: $(var model_path)/ml_package_$(var model_name).param.yaml]
- class_remapper_param_path [default: $(var model_path)/detection_class_remapper.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_ptv3 at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.44.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/autowarefoundation/autoware_universe.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-09-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kenzo Lobos-Tsunekawa
- Amadeusz Szymko
Authors
autoware_ptv3
Purpose
The autoware_ptv3
package is used for 3D lidar segmentation.
Inner-workings / Algorithms
This package implements a TensorRT powered inference node for Point Transformers V3 (PTv3) [1]. The sparse convolution backend corresponds to spconv. Autoware installs it automatically in its setup script. If needed, the user can also build it and install it following the following instructions.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/pointcloud |
sensor_msgs::msg::PointCloud2 |
Input pointcloud topic. |
Output
Name | Type | Description |
---|---|---|
~/output/segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
RGB segmented pointcloud. |
~/output/ground_segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
Pointcloud with the ground segmented out. |
~/output/probs/pointcloud |
sensor_msgs::msg::PointCloud2 |
Class probabilities segmented pointcloud. |
debug/cyclic_time_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Cyclic time (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
PTv3Node node
{{ json_to_markdown(“perception/autoware_ptv3/schema/ptv3.schema.json”) }}
PTv3Node model
{{ json_to_markdown(“perception/autoware_ptv3/schema/ml_package_ptv3.schema.json”) }}
The build_only
option
The autoware_ptv3
node has a build_only
option to build the TensorRT engine file from the specified ONNX file, after which the program exits.
ros2 launch autoware_ptv3 ptv3.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_ptv3
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_ptv3 ptv3.launch.xml log_level:=debug
Assumptions / Known limits
This node assumes that the input pointcloud follows the PointXYZIRC
layout defined in autoware_point_types
.
Trained Models
- v1 – First model release trained with PoC pseudo labels for the internal T4 dataset.
Changelog
References/External links
[1] Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. “Point Transformer V3: Simpler, Faster, Stronger.” 2024 Conference on Computer Vision and Pattern Recognition.
Package Dependencies
System Dependencies
Dependant Packages
Launch files
- launch/ptv3.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- segmented/pointcloud [default: /sensing/lidar/concatenated/segmented]
- ground_segmented/pointcloud [default: /sensing/lidar/concatenated/no_ground]
- probs/pointcloud [default: /sensing/lidar/concatenated/probs]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: ptv3]
- model_path [default: $(var data_path)/ptv3]
- model_param_path [default: $(find-pkg-share autoware_ptv3)/config/$(var model_name).param.yaml]
- ml_package_param_path [default: $(var model_path)/ml_package_$(var model_name).param.yaml]
- class_remapper_param_path [default: $(var model_path)/detection_class_remapper.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_ptv3 at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.44.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/autowarefoundation/autoware_universe.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-09-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kenzo Lobos-Tsunekawa
- Amadeusz Szymko
Authors
autoware_ptv3
Purpose
The autoware_ptv3
package is used for 3D lidar segmentation.
Inner-workings / Algorithms
This package implements a TensorRT powered inference node for Point Transformers V3 (PTv3) [1]. The sparse convolution backend corresponds to spconv. Autoware installs it automatically in its setup script. If needed, the user can also build it and install it following the following instructions.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/pointcloud |
sensor_msgs::msg::PointCloud2 |
Input pointcloud topic. |
Output
Name | Type | Description |
---|---|---|
~/output/segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
RGB segmented pointcloud. |
~/output/ground_segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
Pointcloud with the ground segmented out. |
~/output/probs/pointcloud |
sensor_msgs::msg::PointCloud2 |
Class probabilities segmented pointcloud. |
debug/cyclic_time_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Cyclic time (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
PTv3Node node
{{ json_to_markdown(“perception/autoware_ptv3/schema/ptv3.schema.json”) }}
PTv3Node model
{{ json_to_markdown(“perception/autoware_ptv3/schema/ml_package_ptv3.schema.json”) }}
The build_only
option
The autoware_ptv3
node has a build_only
option to build the TensorRT engine file from the specified ONNX file, after which the program exits.
ros2 launch autoware_ptv3 ptv3.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_ptv3
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_ptv3 ptv3.launch.xml log_level:=debug
Assumptions / Known limits
This node assumes that the input pointcloud follows the PointXYZIRC
layout defined in autoware_point_types
.
Trained Models
- v1 – First model release trained with PoC pseudo labels for the internal T4 dataset.
Changelog
References/External links
[1] Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. “Point Transformer V3: Simpler, Faster, Stronger.” 2024 Conference on Computer Vision and Pattern Recognition.
Package Dependencies
System Dependencies
Dependant Packages
Launch files
- launch/ptv3.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- segmented/pointcloud [default: /sensing/lidar/concatenated/segmented]
- ground_segmented/pointcloud [default: /sensing/lidar/concatenated/no_ground]
- probs/pointcloud [default: /sensing/lidar/concatenated/probs]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: ptv3]
- model_path [default: $(var data_path)/ptv3]
- model_param_path [default: $(find-pkg-share autoware_ptv3)/config/$(var model_name).param.yaml]
- ml_package_param_path [default: $(var model_path)/ml_package_$(var model_name).param.yaml]
- class_remapper_param_path [default: $(var model_path)/detection_class_remapper.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_ptv3 at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.44.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/autowarefoundation/autoware_universe.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-09-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kenzo Lobos-Tsunekawa
- Amadeusz Szymko
Authors
autoware_ptv3
Purpose
The autoware_ptv3
package is used for 3D lidar segmentation.
Inner-workings / Algorithms
This package implements a TensorRT powered inference node for Point Transformers V3 (PTv3) [1]. The sparse convolution backend corresponds to spconv. Autoware installs it automatically in its setup script. If needed, the user can also build it and install it following the following instructions.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/pointcloud |
sensor_msgs::msg::PointCloud2 |
Input pointcloud topic. |
Output
Name | Type | Description |
---|---|---|
~/output/segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
RGB segmented pointcloud. |
~/output/ground_segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
Pointcloud with the ground segmented out. |
~/output/probs/pointcloud |
sensor_msgs::msg::PointCloud2 |
Class probabilities segmented pointcloud. |
debug/cyclic_time_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Cyclic time (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
PTv3Node node
{{ json_to_markdown(“perception/autoware_ptv3/schema/ptv3.schema.json”) }}
PTv3Node model
{{ json_to_markdown(“perception/autoware_ptv3/schema/ml_package_ptv3.schema.json”) }}
The build_only
option
The autoware_ptv3
node has a build_only
option to build the TensorRT engine file from the specified ONNX file, after which the program exits.
ros2 launch autoware_ptv3 ptv3.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_ptv3
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_ptv3 ptv3.launch.xml log_level:=debug
Assumptions / Known limits
This node assumes that the input pointcloud follows the PointXYZIRC
layout defined in autoware_point_types
.
Trained Models
- v1 – First model release trained with PoC pseudo labels for the internal T4 dataset.
Changelog
References/External links
[1] Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. “Point Transformer V3: Simpler, Faster, Stronger.” 2024 Conference on Computer Vision and Pattern Recognition.
Package Dependencies
System Dependencies
Dependant Packages
Launch files
- launch/ptv3.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- segmented/pointcloud [default: /sensing/lidar/concatenated/segmented]
- ground_segmented/pointcloud [default: /sensing/lidar/concatenated/no_ground]
- probs/pointcloud [default: /sensing/lidar/concatenated/probs]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: ptv3]
- model_path [default: $(var data_path)/ptv3]
- model_param_path [default: $(find-pkg-share autoware_ptv3)/config/$(var model_name).param.yaml]
- ml_package_param_path [default: $(var model_path)/ml_package_$(var model_name).param.yaml]
- class_remapper_param_path [default: $(var model_path)/detection_class_remapper.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_ptv3 at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.44.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/autowarefoundation/autoware_universe.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-09-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kenzo Lobos-Tsunekawa
- Amadeusz Szymko
Authors
autoware_ptv3
Purpose
The autoware_ptv3
package is used for 3D lidar segmentation.
Inner-workings / Algorithms
This package implements a TensorRT powered inference node for Point Transformers V3 (PTv3) [1]. The sparse convolution backend corresponds to spconv. Autoware installs it automatically in its setup script. If needed, the user can also build it and install it following the following instructions.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/pointcloud |
sensor_msgs::msg::PointCloud2 |
Input pointcloud topic. |
Output
Name | Type | Description |
---|---|---|
~/output/segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
RGB segmented pointcloud. |
~/output/ground_segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
Pointcloud with the ground segmented out. |
~/output/probs/pointcloud |
sensor_msgs::msg::PointCloud2 |
Class probabilities segmented pointcloud. |
debug/cyclic_time_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Cyclic time (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
PTv3Node node
{{ json_to_markdown(“perception/autoware_ptv3/schema/ptv3.schema.json”) }}
PTv3Node model
{{ json_to_markdown(“perception/autoware_ptv3/schema/ml_package_ptv3.schema.json”) }}
The build_only
option
The autoware_ptv3
node has a build_only
option to build the TensorRT engine file from the specified ONNX file, after which the program exits.
ros2 launch autoware_ptv3 ptv3.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_ptv3
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_ptv3 ptv3.launch.xml log_level:=debug
Assumptions / Known limits
This node assumes that the input pointcloud follows the PointXYZIRC
layout defined in autoware_point_types
.
Trained Models
- v1 – First model release trained with PoC pseudo labels for the internal T4 dataset.
Changelog
References/External links
[1] Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. “Point Transformer V3: Simpler, Faster, Stronger.” 2024 Conference on Computer Vision and Pattern Recognition.
Package Dependencies
System Dependencies
Dependant Packages
Launch files
- launch/ptv3.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- segmented/pointcloud [default: /sensing/lidar/concatenated/segmented]
- ground_segmented/pointcloud [default: /sensing/lidar/concatenated/no_ground]
- probs/pointcloud [default: /sensing/lidar/concatenated/probs]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: ptv3]
- model_path [default: $(var data_path)/ptv3]
- model_param_path [default: $(find-pkg-share autoware_ptv3)/config/$(var model_name).param.yaml]
- ml_package_param_path [default: $(var model_path)/ml_package_$(var model_name).param.yaml]
- class_remapper_param_path [default: $(var model_path)/detection_class_remapper.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_ptv3 at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.44.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/autowarefoundation/autoware_universe.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-09-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kenzo Lobos-Tsunekawa
- Amadeusz Szymko
Authors
autoware_ptv3
Purpose
The autoware_ptv3
package is used for 3D lidar segmentation.
Inner-workings / Algorithms
This package implements a TensorRT powered inference node for Point Transformers V3 (PTv3) [1]. The sparse convolution backend corresponds to spconv. Autoware installs it automatically in its setup script. If needed, the user can also build it and install it following the following instructions.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/pointcloud |
sensor_msgs::msg::PointCloud2 |
Input pointcloud topic. |
Output
Name | Type | Description |
---|---|---|
~/output/segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
RGB segmented pointcloud. |
~/output/ground_segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
Pointcloud with the ground segmented out. |
~/output/probs/pointcloud |
sensor_msgs::msg::PointCloud2 |
Class probabilities segmented pointcloud. |
debug/cyclic_time_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Cyclic time (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
PTv3Node node
{{ json_to_markdown(“perception/autoware_ptv3/schema/ptv3.schema.json”) }}
PTv3Node model
{{ json_to_markdown(“perception/autoware_ptv3/schema/ml_package_ptv3.schema.json”) }}
The build_only
option
The autoware_ptv3
node has a build_only
option to build the TensorRT engine file from the specified ONNX file, after which the program exits.
ros2 launch autoware_ptv3 ptv3.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_ptv3
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_ptv3 ptv3.launch.xml log_level:=debug
Assumptions / Known limits
This node assumes that the input pointcloud follows the PointXYZIRC
layout defined in autoware_point_types
.
Trained Models
- v1 – First model release trained with PoC pseudo labels for the internal T4 dataset.
Changelog
References/External links
[1] Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. “Point Transformer V3: Simpler, Faster, Stronger.” 2024 Conference on Computer Vision and Pattern Recognition.
Package Dependencies
System Dependencies
Dependant Packages
Launch files
- launch/ptv3.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- segmented/pointcloud [default: /sensing/lidar/concatenated/segmented]
- ground_segmented/pointcloud [default: /sensing/lidar/concatenated/no_ground]
- probs/pointcloud [default: /sensing/lidar/concatenated/probs]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: ptv3]
- model_path [default: $(var data_path)/ptv3]
- model_param_path [default: $(find-pkg-share autoware_ptv3)/config/$(var model_name).param.yaml]
- ml_package_param_path [default: $(var model_path)/ml_package_$(var model_name).param.yaml]
- class_remapper_param_path [default: $(var model_path)/detection_class_remapper.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]
Messages
Services
Plugins
Recent questions tagged autoware_ptv3 at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.44.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/autowarefoundation/autoware_universe.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-09-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Kenzo Lobos-Tsunekawa
- Amadeusz Szymko
Authors
autoware_ptv3
Purpose
The autoware_ptv3
package is used for 3D lidar segmentation.
Inner-workings / Algorithms
This package implements a TensorRT powered inference node for Point Transformers V3 (PTv3) [1]. The sparse convolution backend corresponds to spconv. Autoware installs it automatically in its setup script. If needed, the user can also build it and install it following the following instructions.
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/pointcloud |
sensor_msgs::msg::PointCloud2 |
Input pointcloud topic. |
Output
Name | Type | Description |
---|---|---|
~/output/segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
RGB segmented pointcloud. |
~/output/ground_segmented/pointcloud |
sensor_msgs::msg::PointCloud2 |
Pointcloud with the ground segmented out. |
~/output/probs/pointcloud |
sensor_msgs::msg::PointCloud2 |
Class probabilities segmented pointcloud. |
debug/cyclic_time_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Cyclic time (ms). |
debug/pipeline_latency_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Pipeline latency time (ms). |
debug/processing_time/preprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Preprocess (ms). |
debug/processing_time/inference_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Inference time (ms). |
debug/processing_time/postprocess_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Postprocess time (ms). |
debug/processing_time/total_ms |
autoware_internal_debug_msgs::msg::Float64Stamped |
Total processing time (ms). |
Parameters
PTv3Node node
{{ json_to_markdown(“perception/autoware_ptv3/schema/ptv3.schema.json”) }}
PTv3Node model
{{ json_to_markdown(“perception/autoware_ptv3/schema/ml_package_ptv3.schema.json”) }}
The build_only
option
The autoware_ptv3
node has a build_only
option to build the TensorRT engine file from the specified ONNX file, after which the program exits.
ros2 launch autoware_ptv3 ptv3.launch.xml build_only:=true
The log_level
option
The default logging severity level for autoware_ptv3
is info
. For debugging purposes, the developer may decrease severity level using log_level
parameter:
ros2 launch autoware_ptv3 ptv3.launch.xml log_level:=debug
Assumptions / Known limits
This node assumes that the input pointcloud follows the PointXYZIRC
layout defined in autoware_point_types
.
Trained Models
- v1 – First model release trained with PoC pseudo labels for the internal T4 dataset.
Changelog
References/External links
[1] Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. “Point Transformer V3: Simpler, Faster, Stronger.” 2024 Conference on Computer Vision and Pattern Recognition.
Package Dependencies
System Dependencies
Dependant Packages
Launch files
- launch/ptv3.launch.xml
-
- input/pointcloud [default: /sensing/lidar/concatenated/pointcloud]
- segmented/pointcloud [default: /sensing/lidar/concatenated/segmented]
- ground_segmented/pointcloud [default: /sensing/lidar/concatenated/no_ground]
- probs/pointcloud [default: /sensing/lidar/concatenated/probs]
- data_path [default: $(env HOME)/autoware_data]
- model_name [default: ptv3]
- model_path [default: $(var data_path)/ptv3]
- model_param_path [default: $(find-pkg-share autoware_ptv3)/config/$(var model_name).param.yaml]
- ml_package_param_path [default: $(var model_path)/ml_package_$(var model_name).param.yaml]
- class_remapper_param_path [default: $(var model_path)/detection_class_remapper.param.yaml]
- build_only [default: false]
- log_level [default: info]
- use_pointcloud_container [default: false]
- pointcloud_container_name [default: pointcloud_container]