DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars

TL;DR

DeepFusion introduces a flexible, modular multi-modal 3D object detection system that effectively combines lidar, camera, and radar data, enhancing detection accuracy and robustness, especially at long distances and under adverse weather conditions.

Contribution

It presents a novel modular fusion architecture that allows easy exchange of feature extractors and demonstrates improved detection performance across multiple sensor combinations.

Findings

Effective fusion of lidar, camera, and radar improves detection accuracy.

Lidar-camera fusion enhances faraway car detection up to 225 meters.

Robustness against adverse weather depends on lidar point density and accurate depth estimation.

Abstract

We propose DeepFusion, a modular multi-modal architecture to fuse lidars, cameras and radars in different combinations for 3D object detection. Specialized feature extractors take advantage of each modality and can be exchanged easily, making the approach simple and flexible. Extracted features are transformed into bird's-eye-view as a common representation for fusion. Spatial and semantic alignment is performed prior to fusing modalities in the feature space. Finally, a detection head exploits rich multi-modal features for improved 3D detection performance. Experimental results for lidar-camera, lidar-camera-radar and camera-radar fusion show the flexibility and effectiveness of our fusion approach. In the process, we study the largely unexplored task of faraway car detection up to 225 meters, showing the benefits of our lidar-camera fusion. Furthermore, we investigate the required density of lidar points for 3D object detection and illustrate implications at the example of robustness against adverse weather conditions. Moreover, ablation studies on our camera-radar fusion highlight the importance of accurate depth estimation.