  While numerous 3D detection works leverage the complementary relationship
between RGB images and point clouds, developments in the broader framework of
semi-supervised object recognition remain uninfluenced by multi-modal fusion.
Current methods develop independent pipelines for 2D and 3D semi-supervised
learning despite the availability of paired image and point cloud frames.
Observing that the distinct characteristics of each sensor cause them to be
biased towards detecting different objects, we propose DetMatch, a flexible
framework for joint semi-supervised learning on 2D and 3D modalities. By
identifying objects detected in both sensors, our pipeline generates a cleaner,
more robust set of pseudo-labels that both demonstrates stronger performance
and stymies single-modality error propagation. Further, we leverage the richer
semantics of RGB images to rectify incorrect 3D class predictions and improve
localization of 3D boxes. Evaluating on the challenging KITTI and Waymo
datasets, we improve upon strong semi-supervised learning methods and observe
higher quality pseudo-labels. Code will be released at
https://github.com/Divadi/DetMatch
