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While numerous 3D detection works leverage the complementary relationshipbetween RGB images and point clouds, developments in the broader framework ofsemi-supervised object recognition remain uninfluenced by multi-modal fusion.Current methods develop independent pipelines for 2D and 3D semi-supervisedlearning despite the availability of paired image and point cloud frames.Observing that the distinct characteristics of each sensor cause them to bebiased towards detecting different objects, we propose DetMatch, a flexibleframework for joint semi-supervised learning on 2D and 3D modalities. Byidentifying objects detected in both sensors, our pipeline generates a cleaner,more robust set of pseudo-labels that both demonstrates stronger performanceand stymies single-modality error propagation. Further, we leverage the richersemantics of RGB images to rectify incorrect 3D class predictions and improvelocalization of 3D boxes. Evaluating on the challenging KITTI and Waymodatasets, we improve upon strong semi-supervised learning methods and observehigher quality pseudo-labels. Code will be released athttps://github.com/Divadi/DetMatch

DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection