mlsanityData to 14 Oct 2026

VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars

arXiv 2107.02980 ↗First posted 7 Jul 2021cs.CVcited by 6

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Abstract (arXiv)

A unified neural network structure is presented for joint 3D object detection and point cloud segmentation in this paper. We leverage rich supervision from both detection and segmentation labels rather than using just one of them. In addition, an extension based on single-stage object detectors is proposed based on the implicit function widely used in 3D scene and object understanding. The extension branch takes the final feature map from the object detection module as input, and produces an implicit function that generates semantic distribution for each point for its corresponding voxel center. We demonstrated the performance of our structure on nuScenes-lidarseg, a large-scale outdoor dataset. Our solution achieves competitive results against state-of-the-art methods in both 3D object detection and point cloud segmentation with little additional computation load compared with object detection solutions. The capability of efficient weakly supervision semantic segmentation of the proposed method is also validated by experiments.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 43 of its 52 references that are in the map. First posted on arXiv 7 Jul 2021.

Builds on

43 of 52 references.

References · 43

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31 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 0; 2025: 1.

6 papers.