DarkOcc: Illumination-Robust Indoor Occupancy Prediction

Existing RGB-based occupancy models typically assume well-illuminated inputs and suffer substantial performance degradation under low-light conditions. Image enhancement only partially addresses this issue, while jointly training on normal- and low-light images can compromise performance under normal illumination. We present DarkOcc, a framework for robust indoor occupancy prediction across illumination conditions. DarkOcc first learns illumination-robust visual features through multi-level feature adaptation to improve occupancy prediction under low-light conditions. A geometry-guided occupancy completion module then combines these features with base depth and voxel predictions to recover missing occupied structure, addressing occupancy incompleteness exacerbated by low-light degradation. On Occ-ScanNet-mini, DarkOcc improves Geo-IoU over GPOcc trained on mixed-light data (MLA) by 5.57 pp under severe low light and 5.13 pp under normal illumination. Further experiments demonstrate its effectiveness in embodied occupancy prediction. We also construct a real-world indoor occupancy benchmark covering normal and low-light conditions. Under low illumination, DarkOcc improves Geo-IoU over MLA by 9.76 pp.