Papers › SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection

SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection

9 Mar 2024CVPR 2024 1arXiv:2403.05817archive 2025-07-28

Gang Zhang, Junnan Chen, Guohuan Gao, Jianmin Li, Si Liu, Xiaolin Hu

LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However, the computational costs introduced by the dense feature maps grow quadratically as the perception range increases, making these models hard to scale up to long-range detection. Some recent works have attempted to construct fully sparse detectors to solve this issue; nevertheless, the resulting models either rely on a complex multi-stage pipeline or exhibit inferior performance. In this work, we propose SAFDNet, a straightforward yet highly effective architecture, tailored for fully sparse 3D object detection. In SAFDNet, an adaptive feature diffusion strategy is designed to address the center feature missing problem. We conducted extensive experiments on Waymo Open, nuScenes, and Argoverse2 datasets. SAFDNet performed slightly better than the previous SOTA on the first two datasets but much better on the last dataset, which features long-range detection, verifying the efficacy of SAFDNet in scenarios where long-range detection is required. Notably, on Argoverse2, SAFDNet surpassed the previous best hybrid detector HEDNet by 2.6% mAP while being 2.1x faster, and yielded 2.1% mAP gains over the previous best sparse detector FSDv2 while being 1.3x faster. The code will be available at https://github.com/zhanggang001/HEDNet.

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post_act_block_dense zhanggang001/HEDNet/pcdet/models/backbones_3d/spconv_backbone_2d.py official repository ran Apache-2.0 (permissive) · 0d095007c24b14c0 · report
post_act_block_dense_1d zhanggang001/HEDNet/pcdet/models/model_utils/hednet_utils.py official repository ran Apache-2.0 (permissive) · 2817a0301783631c · report
post_act_block_dense_2d zhanggang001/HEDNet/pcdet/models/model_utils/hednet_utils.py official repository ran Apache-2.0 (permissive) · 4d8a9bad93483f4e · report
cfg_from_yaml_file zhanggang001/HEDNet/pcdet/config.py official repository unverified Apache-2.0 (permissive) · 44db2351bcc0bffe · report
compute_fg_mask zhanggang001/HEDNet/pcdet/utils/loss_utils.py official repository unverified Apache-2.0 (permissive) · 65fe32ede00e7dcb · report
get_corner_loss_lidar zhanggang001/HEDNet/pcdet/utils/loss_utils.py official repository unverified Apache-2.0 (permissive) · 1780d388cc532a6d · report
merge_new_config zhanggang001/HEDNet/pcdet/config.py official repository unverified Apache-2.0 (permissive) · 50e8e8cdfc5129f0 · report
neg_loss_cornernet zhanggang001/HEDNet/pcdet/utils/loss_utils.py official repository unverified Apache-2.0 (permissive) · 488b91d67a807558 · report
post_act_block zhanggang001/HEDNet/pcdet/models/backbones_3d/spconv_backbone.py official repository unverified Apache-2.0 (permissive) · 2b4e0558df870bf9 · report
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3D Object DetectionAutonomous DrivingObjectObject Detectionobject-detection

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