| --- |
| license: other |
| tags: |
| - heal |
| - horizon |
| - lidar |
| --- |
| |
| # CenterPoint (PointPillars) |
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| CenterPoint (PointPillars variant) voxelizes lidar point clouds into pillars, learns pillar features via `PillarFeatureNet` and scatters them into a 2D pseudo-image via `PointPillarScatter`, extracts multi-scale features via `SECONDNeck`, and `CenterPointHead` regresses box center, size, orientation, and velocity in an anchor-free manner to output 3D bounding boxes. Training uses CBGS (Class-Balanced Grouping and Sampling) data augmentation. |
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| --- |
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| ## Deployment Metrics |
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| ### Model Parameters |
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| | Model | Model Input | Backbone | Neck | Model Output | |
| |---|---|---|---|---| |
| | CenterPoint | lidar point cloud `(B,N,5)` | PointPillarScatter | SECONDNeck | 3D bounding boxes `(B,N,cls+reg)` | |
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|
| ### Accuracy Metrics |
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| | March | Metric | float | calibration | qat | hbm | |
| | --- | --- | --- | --- | --- | --- | |
| | J6M | NDS | 0.5865 | 0.5703 | 0.5853 | 0.5846 | |
| | | mAP | 0.474 | 0.4487 | 0.4699 | 0.4693 | |
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|
| > Results measured with `march = March.NASH_M` (J6M) configuration. |
| > |
| > HEAL version: heal 0.0.2 / hbdk4-compiler 4.11.11 / horizon_plugin_pytorch 3.3.10. |
| |
| ### Performance Metrics |
| |
| > **Performance benchmark**: FPS is measured with single-core 8 threads; latency is single-core single-thread; memory is peak DDR usage. |
| |
| | March | latency (ms) | fps | Memory Usage (MB) | |
| |---|---|---|---| |
| | J6M | 9.23 | 183.98 | 51.10 | |
| | J6P | 7.56 | 833.52 | 47.20 | |
| | J6B | - | - | - | |
| |
| J6B performance is not available for this model. |
| |
| --- |
| |
| ## Model Overview |
| |
| ### Core Design |
| |
| CenterPoint (PointPillars variant) voxelizes lidar point clouds into pillars, learns pillar features via `PillarFeatureNet` and scatters them into a 2D pseudo-image via `PointPillarScatter`, extracts multi-scale features via `SECONDNeck`, and `CenterPointHead` regresses box center, size, orientation, and velocity in an anchor-free manner to output 3D bounding boxes. Training uses CBGS (Class-Balanced Grouping and Sampling) data augmentation. |
| |
| - **Task type**: 3D object detection (3D Object Detection, lidar). |
| - **backbone**: `PillarFeatureNet` (`num_input_features=5`, `num_filters=(64)`, learns pillar features) + `PointPillarScatter` (`num_input_features=64`, `use_horizon_pillar_scatter=True`, scatters pillar features into 2D pseudo-image). |
| - **neck**: `SECONDNeck` (`in_feature_channel=64`, `down_layer_nums=[3,5,5]`, `down_layer_channels=[64,128,256]`, `up_layer_channels=[128,128,128]`, multi-scale feature extraction). |
| - **Detection head**: `CenterPointHead` (anchor-free, `common_heads=dict(reg=(2,2), height=(1,2), dim=(3,2), rot=(2,2), vel=(2,2))`, `with_velocity=True`). |
| - **Loss function**: `CenterPointLoss` (`GaussianFocalLoss` cls + `L1Loss` reg). |
| - **Model input**: lidar point cloud `(B,N,5)` (`point_cloud_range=[-51.2,-51.2,-5.0,51.2,51.2,3.0]`, `voxel_size=[0.2,0.2,8]`, `max_num_points=20`, `max_voxels=(30000,40000)`, `load_dim=5`, `use_dim=[0,1,2,3,4]`, `num_sweeps=9`). |
| - **Model output**: 10-class 3D bounding boxes (`class_names` = `[car, truck, construction_vehicle, bus, trailer, barrier, motorcycle, bicycle, pedestrian, traffic_cone]`), `(B,N,cls+reg)`. |
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| ### Official Repo and Paper |
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| Official repo: https://github.com/tianweiy/CenterPoint |
| Paper: https://arxiv.org/abs/2006.11275 |
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| ### Reference |
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| For more J6 chip deployment details, see https://developer.horizon.auto/blog/14088 |
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