Papers › VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

20 Mar 2023CVPR 2023 1arXiv:2303.11301archive 2025-07-28

Yukang Chen, Jianhui Liu, Xiangyu Zhang, Xiaojuan Qi, Jiaya Jia

3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified and processed by dense prediction heads, which inevitably costs extra computation. In this paper, we instead propose VoxelNext for fully sparse 3D object detection. Our core insight is to predict objects directly based on sparse voxel features, without relying on hand-crafted proxies. Our strong sparse convolutional network VoxelNeXt detects and tracks 3D objects through voxel features entirely. It is an elegant and efficient framework, with no need for sparse-to-dense conversion or NMS post-processing. Our method achieves a better speed-accuracy trade-off than other mainframe detectors on the nuScenes dataset. For the first time, we show that a fully sparse voxel-based representation works decently for LIDAR 3D object detection and tracking. Extensive experiments on nuScenes, Waymo, and Argoverse2 benchmarks validate the effectiveness of our approach. Without bells and whistles, our model outperforms all existing LIDAR methods on the nuScenes tracking test benchmark.

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cfg_from_yaml_file dvlab-research/VoxelNeXt/pcdet/config.py official repository unverified Apache-2.0 (permissive) · 44db2351bcc0bffe · report
compute_fg_mask dvlab-research/VoxelNeXt/pcdet/utils/loss_utils.py official repository unverified Apache-2.0 (permissive) · 65fe32ede00e7dcb · report
get_corner_loss_lidar dvlab-research/VoxelNeXt/pcdet/utils/loss_utils.py official repository unverified Apache-2.0 (permissive) · 1780d388cc532a6d · report
merge_new_config dvlab-research/VoxelNeXt/pcdet/config.py official repository unverified Apache-2.0 (permissive) · 50e8e8cdfc5129f0 · report
neg_loss_cornernet dvlab-research/VoxelNeXt/pcdet/utils/loss_utils.py official repository unverified Apache-2.0 (permissive) · 488b91d67a807558 · report
post_act_block dvlab-research/VoxelNeXt/pcdet/models/backbones_3d/spconv_backbone_voxelnext.py official repository unverified Apache-2.0 (permissive) · 2b4e0558df870bf9 · report

Tasks

3D Object DetectionObjectObject Detectionobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Multi-Object Tracking nuScenes LiDAR only VoxelNeXt AMOTA 71.0 #1 of 1 Archive leaderboard report
3D Object Detection Argoverse2 VoxelNeXt mAP 30.7 #2 of 2 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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