Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection

TL;DR

This paper introduces Multi-Sem Fusion, a multimodal semantic fusion framework that combines 2D image and 3D point cloud data with adaptive attention and deep feature fusion to significantly improve 3D object detection accuracy in autonomous driving.

Contribution

The paper proposes a novel multi-modal fusion framework with adaptive attention and deep feature fusion, addressing misalignment issues and enhancing detection performance over existing methods.

Findings

Achieves state-of-the-art results on nuScenes benchmark.

Significantly outperforms methods using only point clouds or 2D images.

Demonstrates improved detection accuracy through semantic and deep feature fusion.

Abstract

LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy. Nevertheless, the restricted resolution of 2D feature maps impedes accurate re-projection and often induces a pronounced boundary-blurring effect, which is primarily attributed to erroneous semantic segmentation. To well handle this limitation, we propose a general multi-modal fusion framework Multi-Sem Fusion (MSF) to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, we employ 2D/3D semantic segmentation methods to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further reprojected into the 3D point clouds with calibration parameters. To handle the misalignment between the 2D and 3D parsing results, we propose an Adaptive Attention-based Fusion (AAF) module to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a Deep Feature Fusion (DFF) module to aggregate deep features at different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing them with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets state-of-the-art results on the nuScenes testing benchmark.