MoCA3D: Monocular 3D Bounding Box Prediction in the Image Plane

Monocular 3D object understanding has largely been cast asa 2D RoI-to-3D box lifting problem. However, emerging downstream ap-plications require image-plane geometry (e.g., projected 3D box corners)which cannot be easily obtained without known intrinsics, a problem forobject detection in the wild. We introduce MoCA3D, a Monocular,Class-Agnostic 3D model that predicts projected 3D bounding box cor-ners and per-corner depths without requiring camera intrinsics at in-ference time. MoCA3D formulates pixel-space localization and depthassignment as dense prediction via corner heatmaps and depth maps.To evaluate image-plane geometric fidelity, we propose Pixel-AlignedGeometry (PAG), which directly measures image-plane corner anddepth consistency. Extensive experiments under an oracle-2D box pro-tocol demonstrate that MoCA3D achieves state-of-the-art performance,improving image-plane corner PAG by 22.8% while remaining compa-rable on 3D IoU, using up to 57× fewer trainable parameters. Finally,we apply MoCA3D to downstream tasks which were previously imprac-tical under unknown intrinsics, highlighting its utility beyond standardbaseline models. Project page: https://jeoncwcw.github.io/moca3d/.