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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 beexchanged easily, making the approach simple and flexible. Extracted featuresare transformed into bird's-eye-view as a common representation for fusion.Spatial and semantic alignment is performed prior to fusing modalities in thefeature space. Finally, a detection head exploits rich multi-modal features forimproved 3D detection performance. Experimental results for lidar-camera,lidar-camera-radar and camera-radar fusion show the flexibility andeffectiveness of our fusion approach. In the process, we study the largelyunexplored task of faraway car detection up to 225 meters, showing the benefitsof our lidar-camera fusion. Furthermore, we investigate the required density oflidar points for 3D object detection and illustrate implications at the exampleof robustness against adverse weather conditions. Moreover, ablation studies onour camera-radar fusion highlight the importance of accurate depth estimation.

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