Shape-Prior-Based Point Cloud Completion for Single-Stage Fully Sparse 3D Object Detection
Single-stage fully sparse 3D object detectors rely on point clouds data to detect objects in autonomous driving scenarios. However, the sparsity and incompleteness of point clouds significantly limit the performance of 3D object detection. To address this issue, this paper proposes a point clouds completion method specifically designed for single-stage fully sparse detectors. The entire shape-prior-based completion process consists of two consecutive steps. In the first step, we design a novel Instance Selection module, which is capable of identifying point clouds corresponding to foreground objects even when the baseline model does not generate proposals, while effectively ignoring the p...
| Authors | Kaizheng Wang, Mingqian Ji, Jian Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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