# LVIC: Multi-modality segmentation by Lifting Visual Info as Cue

Source: [LVIC: Multi-modality segmentation by Lifting Visual Info as Cue](<https://www.sota2.com/research/paper/68fa727c1e209131cea56b05>)

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

Data updated: 2025-10-26T02:27:04.021Z

Authors: Zichao Dong, Bowen Pang, Xufeng Huang, Hang Ji, Xin Zhan, Junbo Chen

Published: 2024-03-08T08:52:55.000Z

## Abstract

Multi-modality fusion is proven an effective method for 3d perception for autonomous driving. However, most current multi-modality fusion pipelines for LiDAR semantic segmentation have complicated fusion mechanisms. Point painting is a quite straight forward method which directly bind LiDAR points with visual information. Unfortunately, previous point painting like methods suffer from projection error between camera and LiDAR. In our experiments, we find that this projection error is the devil in point painting. As a result of that, we propose a depth aware point painting mechanism, which significantly boosts the multi-modality fusion. Apart from that, we take a deeper look at the desired visual feature for LiDAR to operate semantic segmentation. By Lifting Visual Information as Cue, LVIC ranks 1st on nuScenes LiDAR semantic segmentation benchmark. Our experiments show the robustness and effectiveness. Codes would be make publicly available soon.

## 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/2403.05159>)

[PDF](<https://arxiv.org/pdf/2403.05159.pdf>)