| --- |
| license: other |
| tags: |
| - heal |
| - horizon |
| - lane-detection |
| --- |
| |
| # SparseMapTR+HENet |
|
|
| SparseMapTR uses HENet as the camera backbone to extract multi-view features, converts them to BEV features, then feeds them to SparseMapHead (6-layer sparse query stack) for vectorized map element prediction. Unlike MapTR, SparseMapTR uses a sparse query mechanism (`InstanceBankOE` + `SparsePoint3DEncoder`), refining only a small set of candidate queries iteratively to reduce compute. This task has `use_lidar_gt=True`. |
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| --- |
|
|
| ## Deployment Metrics |
|
|
| ### Model Parameters |
|
|
| | Model | Model Input | Backbone | Neck | Model Output | |
| |---|---|---|---|---| |
| | SparseMapTR | 6-camera multi-view images `(B,6,3,256,704)` + lidar point cloud `(B,N,5)` | HENet-tiny | FPN | vectorized map `(B,L,P,2)` | |
|
|
| ### Accuracy Metrics |
|
|
| | March | Metric | float | calibration | qat | hbm | |
| | --- | --- | --- | --- | --- | --- | |
| | J6M | chamfer mAP (MAP) | 0.5924 | 0.5882 | — | 0.5892 | |
|
|
| > Data tested with `march = March.NASH_M` (J6M); this task has no QAT stage (`—` in the qat column). |
| > |
| > HEAL versions: heal 0.0.2 / hbdk4-compiler 4.11.11 / horizon_plugin_pytorch 3.3.10. |
| |
| ### Performance Metrics |
| |
| > **Performance test methodology**: FPS for J6M/J6P is measured with 8 threads on a single core; J6B uses 4 threads on a single core; Latency is measured with single core, single thread; Memory is peak DDR usage. |
| |
| | March | latency (ms) | fps | Memory Usage | |
| |---|---|---|---| |
| | J6M | 11.46 | 89.46 | 68.40 | |
| | J6P | 9.32 | 259.76 | 98.80 | |
| | J6B | 160.03 | 17.03 | 120.00 | |
| |
| --- |
| |
| ## Model Overview |
| |
| ### Core Design |
| |
| SparseMapTR uses HENet as the camera backbone to extract multi-view features, converts them to BEV features, then feeds them to SparseMapHead (6-layer sparse query stack) for vectorized map element prediction. Unlike MapTR, SparseMapTR uses a sparse query mechanism (`InstanceBankOE` + `SparsePoint3DEncoder`), refining only a small set of candidate queries iteratively to reduce compute. This task has `use_lidar_gt=True`. |
| |
| - **Task type**: Sparse Vectorized Map Construction. |
| - **backbone**: HENet-tiny (pretrained), extracts multi-view camera features. |
| - **neck**: FPN (`out_strides=[4,8,16,32]`, outputs 256-dim multi-scale features). |
| - **decoder**: `SparseMapHead` (6-layer sparse query stack, `InstanceBankOE` + `SparsePoint3DEncoder`). |
| - **map elements**: `map_classes=[divider, ped_crossing, boundary]`, `fixed_ptsnum_per_gt_line=20`. |
| - **BEV range**: `use_lidar_gt=True` branch, `point_cloud_range=[-15.0,-30.0,-10.0,15.0,30.0,10.0]`, `bev_h_=100`, `bev_w_=50` (bev 100×50). |
| - **Model input**: 6-camera images `(B,6,3,256,704)` + lidar point cloud `(B,N,D)` (`use_lidar_gt=True`). |
| - **Model output**: 3 classes of vectorized map elements (divider/ped_crossing/boundary), 20 points per line. |
| |
| ### Official Repo and Paper |
| |
| Official repo: https://github.com/hustvl/MapTR |
| Paper: https://arxiv.org/abs/2208.14437 |
| |
| Note: The camera backbone HENet is developed in HEAL; the official repo uses a different backbone. |
| |
| ### Reference |
| |
| For more J6 chip deployment details, see https://developer.horizon.auto/blog/14100 |
| |
