| --- |
| license: other |
| tags: |
| - heal |
| - horizon |
| - bev |
| - lane-detection |
| --- |
| |
| # MapTR+HENet (BevFormer) |
|
|
| MapTR uses HENet as the camera backbone to extract multi-view features, transforms them to BEV features via BevFormer's single-frame ViewTransformer and BEV Encoder, then feeds BEV features to the MapTR decoder with fixed-point polyline queries (`fixed_ptsnum_per_pred_line=20`) to predict vectorized map elements (divider/ped_crossing/boundary). This task has `use_lidar_gt=False`; map GT is generated online. |
| |
| --- |
| |
| ## Deployment Metrics |
| |
| ### Model Parameters |
| |
| | Model | Model Input | Backbone | Neck | Model Output | |
| |---|---|---|---|---| |
| | MapTR | 6-camera multi-view images `(B,6,3,480,800)` | HENet-tiny | FPN | vectorized map `(B,L,P,2)` | |
| |
| ### Accuracy Metrics |
| |
| | March | Metric | float | calibration | qat | hbm | |
| | --- | --- | --- | --- | --- | --- | |
| | J6M | chamfer mAP (MAP) | 0.6626 | 0.6588 | — | 0.6315 | |
| |
| > 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 is measured with 8 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 | 9.30 | 111.22 | 88.40 | |
| | J6P | 6.16 | 663.68 | 83.30 | |
| | J6B | 33.28 | 30.85 | 80.00 | |
|
|
| --- |
|
|
| ## Model Overview |
|
|
| ### Core Design |
|
|
| MapTR uses HENet as the camera backbone to extract multi-view features, transforms them to BEV features via BevFormer's single-frame ViewTransformer and BEV Encoder, then feeds BEV features to the MapTR decoder with fixed-point polyline queries (`fixed_ptsnum_per_pred_line=20`) to predict vectorized map elements (divider/ped_crossing/boundary). This task has `use_lidar_gt=False`; map GT is generated online. |
| |
| - **Task type**: Online Vectorized Map Construction. |
| - **backbone**: HENet-tiny (pretrained), extracts multi-view camera features. |
| - **neck**: FPN. |
| - **view transformation**: `SingleFrameBevFormerViewTransformer` + `SingleFrameBEVFormerEncoder` (`queue_length=1`/`test_queue_length=1`). |
| - **map elements**: `map_classes=[divider, ped_crossing, boundary]`, `fixed_ptsnum_per_gt_line=20`. |
| - **BEV range**: `use_lidar_gt=False` else branch, `point_cloud_range=[-30.0,-15.0,-10.0,30.0,15.0,10.0]`, `bev_h_=50`, `bev_w_=100` (bev 50×100). |
| - **Model input**: 6-camera single-frame images `(B,6,3,480,800)` . |
| - **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 |
| |
