OpenExploer/flashocc_henet_lss_occ3d

FlashOcc + HENet + LSS

FlashOcc uses LSS (Lift-Splat-Shoot) view transformation: HENet extracts multi-view camera features; LSSTransformer predicts depth distribution and lifts 2D features to 3D voxel space (depth=45, num_points=10, bev_size=(40,40,0.625)); fused via BevEncoder (BiFPN); FlashOccDetDecoder/BEVOCCHead2D outputs 18-class 3D occupancy predictions.

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Deployment Metrics

Model Parameters

Model	Model Input	Backbone	Neck	Model Output
FlashOcc	6-camera multi-view images (B,6,3,512,960)	HENet	FPN + LSSTransformer + BevEncoder	Occupancy grid (B,C,H,W)

Accuracy Metrics

March	Metric	float	calibration	qat	hbm
J6M	Occ mIoU (MeanIOU)	0.3664	0.3688	—	0.369

Results are based on 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 measurement: FPS is measured with single-core eight-thread; Latency is measured with single-core single-thread; Memory is peak DDR usage.

March	latency (ms)	fps	Memory Usage
J6M	7.65	136.07	73.80
J6P	5.62	731.18	78.10
J6B	30.51	33.66	82.00

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Model Overview

Core Design

FlashOcc uses LSS (Lift-Splat-Shoot) view transformation: HENet extracts multi-view camera features; LSSTransformer predicts depth distribution and lifts 2D features to 3D voxel space (depth=45, num_points=10, bev_size=(40,40,0.625)); fused via BevEncoder (BiFPN); FlashOccDetDecoder/BEVOCCHead2D outputs 18-class 3D occupancy predictions.

• Task type: BEV 3D occupancy prediction (BEV Occupancy Prediction).
• backbone: HENet (type=HENet, depth=45, num_points=10), extracts multi-view camera features.
• neck: FPN + LSSTransformer (Lift-Splat-Shoot view transformation, bev_size=(40,40,0.625), grid_size=(128,128)) + BevEncoder (BiFPN).
• Occupancy head: FlashOccDetDecoder (BEVOCCHead2D, num_classes=18, ignore_index=17).
• Loss: CrossEntropyLoss (occ seg).
• Model input: 6-view camera images (B,6,3,512,960) (data_shape=(3,512,960)).
• Model output: 18-class 3D occupancy grid (B,C,H,W) (occ3d_seg_class 18 classes, includes others/ignore_index=17).

Official Repo and Paper

Official repo: https://github.com/Yzichen/FlashOCC Paper: https://arxiv.org/abs/2311.12058

Note: camera backbone HENet is HEAL-developed; official repo uses a different backbone.

Reference

For more J6 chip deployment details, see https://developer.horizon.auto/blog/10154