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We present PointFusion, a generic 3D object detection method that leveragesboth image and 3D point cloud information. Unlike existing methods that eitheruse multi-stage pipelines or hold sensor and dataset-specific assumptions,PointFusion is conceptually simple and application-agnostic. The image data andthe raw point cloud data are independently processed by a CNN and a PointNetarchitecture, respectively. The resulting outputs are then combined by a novelfusion network, which predicts multiple 3D box hypotheses and theirconfidences, using the input 3D points as spatial anchors. We evaluatePointFusion on two distinctive datasets: the KITTI dataset that featuresdriving scenes captured with a lidar-camera setup, and the SUN-RGBD datasetthat captures indoor environments with RGB-D cameras. Our model is the firstone that is able to perform better or on-par with the state-of-the-art on thesediverse datasets without any dataset-specific model tuning.

PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation