Behind the Veil: Enhanced Indoor 3D Scene Reconstruction with Occluded Surfaces Completion
<|reference_start|>Behind the Veil: Enhanced Indoor 3D Scene Reconstruction with Occluded Surfaces Completion: In this paper, we present a novel indoor 3D reconstruction method with occluded surface completion, given a sequence of depth readings. Prior state-of-the-art (SOTA) methods only focus on the reconstruction of the visible areas in a scene, neglecting the invisible areas due to the occlusions, e.g., the contact surface between furniture, occluded wall and floor. Our method tackles the task of completing the occluded scene surfaces, resulting in a complete 3D scene mesh. The core idea of our method is learning 3D geometry prior from various complete scenes to infer the occluded geometry of an unseen scene from solely depth measurements. We design a coarse-fine hierarchical octree representation coupled with a dual-decoder architecture, i.e., Geo-decoder and 3D Inpainter, which jointly reconstructs the complete 3D scene geometry. The Geo-decoder with detailed representation at fine levels is optimized online for each scene to reconstruct visible surfaces. The 3D Inpainter with abstract representation at coarse levels is trained offline using various scenes to complete occluded surfaces. As a result, while the Geo-decoder is specialized for an individual scene, the 3D Inpainter can be generally applied across different scenes. We evaluate the proposed method on the 3D Completed Room Scene (3D-CRS) and iTHOR datasets, significantly outperforming the SOTA methods by a gain of 16.8% and 24.2% in terms of the completeness of 3D reconstruction. 3D-CRS dataset including a complete 3D mesh of each scene is provided at project webpage.<|reference_end|>
@article{sun2024behind,
    title={Behind the Veil: Enhanced Indoor 3D Scene Reconstruction with Occluded
  Surfaces Completion},
    author={Su Sun, Cheng Zhao, Yuliang Guo, Ruoyu Wang, Xinyu Huang, Yingjie
  Victor Chen, Liu Ren},
    journal={arXiv preprint arXiv:2404.03070},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03070},
    primaryClass={cs.CV}
}