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Papers/Monocular Occupancy Prediction for Scalable Indoor Scenes

Monocular Occupancy Prediction for Scalable Indoor Scenes

Hongxiao Yu, Yuqi Wang, Yuntao Chen, Zhaoxiang Zhang

2024-07-16Prediction3D Semantic Scene Completion from a single RGB image
PaperPDFCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
ReconstructionNYUv2mIoU31.25ISO
3D ReconstructionNYUv2mIoU31.25ISO
3DNYUv2mIoU31.25ISO
3D Semantic Scene CompletionNYUv2mIoU31.25ISO
3D Scene ReconstructionNYUv2mIoU31.25ISO
Single-View 3D ReconstructionNYUv2mIoU31.25ISO

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