DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars
This work addresses the problem of robust 3D object detection for autonomous vehicles by enabling flexible sensor fusion, though it is incremental as it builds on existing multi-modal approaches.
The authors tackled 3D object detection by proposing DeepFusion, a modular multi-modal architecture that fuses lidars, cameras, and radars in various combinations, achieving improved performance as demonstrated in experiments for lidar-camera, lidar-camera-radar, and camera-radar fusion, including faraway car detection up to 225 meters.
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.