TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

TL;DR

TransFusion introduces a transformer-based LiDAR-camera fusion method that adaptively combines sensor data, enhancing robustness against poor image conditions and calibration errors in 3D object detection for autonomous driving.

Contribution

It proposes a novel soft-association fusion mechanism using transformers, improving robustness and accuracy over existing hard-association methods.

Findings

Achieves state-of-the-art performance on large-scale datasets.

Demonstrates robustness against degraded image quality and calibration errors.

Extends effectively to 3D tracking, winning first place in nuScenes leaderboard.

Abstract

LiDAR and camera are two important sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, the robustness against inferior image conditions, e.g., bad illumination and sensor misalignment, is under-explored. Existing fusion methods are easily affected by such conditions, mainly due to a hard association of LiDAR points and image pixels, established by calibration matrices. We propose TransFusion, a robust solution to LiDAR-camera fusion with a soft-association mechanism to handle inferior image conditions. Specifically, our TransFusion consists of convolutional backbones and a detection head based on a transformer decoder. The first layer of the decoder predicts initial bounding boxes from a LiDAR point cloud using a sparse set of object queries, and its second decoder layer adaptively fuses the object queries with useful image features, leveraging both spatial and contextual relationships. The attention mechanism of the transformer enables our model to adaptively determine where and what information should be taken from the image, leading to a robust and effective fusion strategy. We additionally design an image-guided query initialization strategy to deal with objects that are difficult to detect in point clouds. TransFusion achieves state-of-the-art performance on large-scale datasets. We provide extensive experiments to demonstrate its robustness against degenerated image quality and calibration errors. We also extend the proposed method to the 3D tracking task and achieve the 1st place in the leaderboard of nuScenes tracking, showing its effectiveness and generalization capability.