  Camera-based 3D occupancy prediction has recently garnered increasing
attention in outdoor driving scenes. However, research in indoor scenes remains
relatively unexplored. The core differences in indoor scenes lie in the
complexity of scene scale and the variance in object size. In this paper, we
propose a novel method, named ISO, for predicting indoor scene occupancy using
monocular images. ISO harnesses the advantages of a pretrained depth model to
achieve accurate depth predictions. Furthermore, we introduce the Dual Feature
Line of Sight Projection (D-FLoSP) module within ISO, which enhances the
learning of 3D voxel features. To foster further research in this domain, we
introduce Occ-ScanNet, a large-scale occupancy benchmark for indoor scenes.
With a dataset size 40 times larger than the NYUv2 dataset, it facilitates
future scalable research in indoor scene analysis. Experimental results on both
NYUv2 and Occ-ScanNet demonstrate that our method achieves state-of-the-art
performance. The dataset and code are made publicly at
https://github.com/hongxiaoy/ISO.git.
