3D occupancy prediction — estimating which voxels in a scene are occupied and by what — underpins autonomous driving perception. But existing methods require precise camera calibration and in-domain training data. Deploy in a new city with different cameras, and performance collapses.
The authors (arXiv:2603.23502) introduce OccAny, which predicts metric occupancy with segmentation features from uncalibrated, out-of-domain scenes. It works with sequential, monocular, or surround-view inputs without retraining.
Two enabling techniques: Segmentation Forcing, which conditions occupancy prediction on instance-level segmentation and improves quality while enabling mask-level outputs. And Novel View Rendering, which synthesizes additional viewpoints at test time to fill geometric gaps — particularly effective for monocular inputs where only one perspective is available.
The through-claim: the calibration dependence of prior 3D prediction methods was a training artifact, not a fundamental requirement. The geometric information needed for occupancy prediction exists in the image content, not in the camera parameters. OccAny demonstrates that with the right architecture, the scene itself provides sufficient geometric constraints, even without knowing how the camera was pointed.