Papers › Monocular Occupancy Prediction for Scalable Indoor Scenes

Monocular Occupancy Prediction for Scalable Indoor Scenes

16 Jul 2024arXiv:2407.11730archive 2025-07-28

Hongxiao Yu, Yuqi Wang, Yuntao Chen, Zhaoxiang Zhang

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.

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compute_super_CP_multilabel_loss hongxiaoy/ISO/iso/loss/CRP_loss.py official repository ran Apache-2.0 (permissive) · 488de7dc43489343 · report
get_depth_index hongxiaoy/ISO/iso/models/modules.py official repository ran Apache-2.0 (permissive) · a99e276e1b9e7530 · report
get_iou hongxiaoy/ISO/iso/loss/sscMetrics.py official repository ran fingerprinted Apache-2.0 (permissive) · 251f4945130ffbc3 · report
sem_scal_loss hongxiaoy/ISO/iso/loss/ssc_loss.py official repository ran Apache-2.0 (permissive) · d062b8e225df0d31 · report
KL_sep hongxiaoy/ISO/iso/loss/ssc_loss.py official repository unverified Apache-2.0 (permissive) · 033da7a511cc1166 · report
bin_depths hongxiaoy/ISO/iso/models/modules.py official repository unverified Apache-2.0 (permissive) · cee458c6cb15d593 · report
geo_scal_loss hongxiaoy/ISO/iso/loss/ssc_loss.py official repository unverified Apache-2.0 (permissive) · 1cf8e8cd991e96bb · report
get_accuracy hongxiaoy/ISO/iso/loss/sscMetrics.py official repository unverified Apache-2.0 (permissive) · 40d8d0dfd5686afe · report
sample_grid_feature hongxiaoy/ISO/iso/models/modules.py official repository unverified Apache-2.0 (permissive) · e3e9b85784ce26ed · report

Tasks

3D Semantic Scene Completion from a single RGB imagePrediction

Datasets

Introduced by this paper, per the archive.

OccScanNet

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Semantic Scene Completion from a single RGB image NYUv2 ISO mIoU 31.25 #1 of 6 Archive leaderboard report

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Methods

AttentionSoftmax

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