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

Source: [VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars](<https://www.sota2.com/research/paper/68fa6b321e209131cea2ccb3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:50.640Z

Authors: Yuanxin Zhong, Minghan Zhu, Huei Peng

Published: 2021-07-07T02:16:20.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2107.02980>)

[PDF](<https://arxiv.org/pdf/2107.02980.pdf>)