| --- |
| license: other |
| tags: |
| - heal |
| - horizon |
| - bev |
| - lidar |
| --- |
| |
| # BevFusion+PointPillar+HENet Multisensor Multitask |
|
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| The BevFusion multisensor multitask model extracts BEV features via dual branches: the camera branch uses HENet to extract multi-view features, then BevFormer ViewTransformer to BEV; the lidar branch uses PointPillars (`PillarFeatureNet` + `PointPillarScatter` + `SECONDNeck`) to voxelize point clouds and extract BEV features. Fused BEV features feed CenterPoint detection head (3D object detection) and occupancy head (semantic occupancy prediction) for joint det+occ training. |
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| --- |
|
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| ## Deployment Metrics |
|
|
| ### Model Parameters |
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|
| | Model | Model Input | Backbone | Neck | Model Output | |
| |---|---|---|---|---| |
| | BevFusion | 6-camera multi-view images `(B,6,3,512,960)` + lidar point cloud `(B,N,5)` | PointPillar (lidar) + HENet (camera) | FPN (camera) + SECONDNeck (lidar) | det bounding boxes `(B,N,cls+reg)`; occ occupancy grid `(B,C,H,W)` | |
|
|
| ### Accuracy Metrics |
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|
| | March | Metric | float | calibration | qat | hbm | |
| | --- | --- | --- | --- | --- | --- | |
| | J6M | NDS | 0.6421 | 0.6301 | — | 0.6294 | |
| | | mAP | 0.5825 | 0.5724 | — | 0.5726 | |
| | | Occ mIoU | 0.5187 | 0.52 | — | 0.5206 | |
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|
| > Results measured with `march = March.NASH_M` (J6M) configuration; this task has no QAT stage (qat column is `—`). |
| > |
| > 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 | |
| |---|---|---|---| |
| | J6M | 23.93 | 49.41 | 187.10 | |
| | J6P | 16.87 | 281.67 | 195.70 | |
| | J6B | - | - | - | |
| |
| J6B performance is not available for this model. |
| |
| --- |
| |
| ## Model Overview |
| |
| ### Core Design |
| |
| The BevFusion multisensor multitask model extracts BEV features via dual branches: the camera branch uses HENet to extract multi-view features, then BevFormer ViewTransformer to BEV; the lidar branch uses PointPillars (`PillarFeatureNet` + `PointPillarScatter` + `SECONDNeck`) to voxelize point clouds and extract BEV features. Fused BEV features feed CenterPoint detection head (3D object detection) and occupancy head (semantic occupancy prediction) for joint det+occ training. |
| |
| - **Task type**: Multisensor multitask fusion (3D object detection + occupancy grid prediction). |
| - **backbone**: Camera HENet (multi-view feature extraction) + lidar PointPillars (`PillarFeatureNet` + `PointPillarScatter`). |
| - **neck**: Camera FPN + lidar SECONDNeck. |
| - **Detection head**: CenterPoint detection head, outputting 10-class 3D bounding boxes + velocity (`num_classes=10`). |
| - **Occupancy head**: Semantic occupancy prediction, 18 classes (`num_classes_occ=18`). |
| - **BEV range**: `bev_size=(51.2, 51.2, 0.8)`, `bev_size_occ=(40, 40)`; `point_cloud_range=[-51.2,-51.2,-5.0,51.2,51.2,3.0]`. |
| - **Model input**: 6-camera multi-view images (B,6,3,512,960) + lidar point cloud (B,N,D). |
| - **Model output**: det 3D bounding boxes + occ occupancy grid semantics. |
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| **Deployment note**: HBIR export enables `enable_vpu=True`; compilation uses `input_source="ddr, ddr, pyramid, ddr, ddr, ddr, ddr"` (DDR preferred; lidar features read from DDR). |
|
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| ### Official Repo and Paper |
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| Official repo: https://github.com/mit-han-lab/bevfusion |
| Paper: https://arxiv.org/abs/2205.13542 |
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| Note: Camera branch is BevFormer, lidar branch is PointPillars/CenterPoint; camera backbone HENet is a HEAL in-house implementation; the official repo uses a different backbone. |
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| ### Reference |
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| For more J6 chip deployment details, see https://developer.horizon.auto/blog/14092 |
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