# InsFusion: Rethink Instance-level LiDAR-Camera Fusion for 3D Object Detection

Source: [InsFusion: Rethink Instance-level LiDAR-Camera Fusion for 3D Object Detection](<https://www.sota2.com/research/paper/68fa7b471e209131cea877cd>)

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

Data updated: 2025-11-19T05:02:23.880Z

Authors: Zhongyu Xia, Hansong Yang, Yongtao Wang

Published: 2025-09-10T08:12:15.000Z

## Abstract

Three-dimensional Object Detection from multi-view cameras and LiDAR is a crucial component for autonomous driving and smart transportation. However, in the process of basic feature extraction, perspective transformation, and feature fusion, noise and error will gradually accumulate. To address this issue, we propose InsFusion, which can extract proposals from both raw and fused features and utilizes these proposals to query the raw features, thereby mitigating the impact of accumulated errors. Additionally, by incorporating attention mechanisms applied to the raw features, it thereby mitigates the impact of accumulated errors. Experiments on the nuScenes dataset demonstrate that InsFusion is compatible with various advanced baseline methods and delivers new state-of-the-art performance for 3D object detection.

## 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/2509.08374>)

[PDF](<https://arxiv.org/pdf/2509.08374.pdf>)