mlsanityData to 14 Oct 2026

CompNVS: Novel View Synthesis with Scene Completion

arXiv 2207.11467 ↗First posted 23 Jul 2022cs.CV cs.AIcited by 9

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Abstract (arXiv)

We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage. While generative neural approaches have demonstrated spectacular results on 2D images, they have not yet achieved similar photorealistic results in combination with scene completion where a spatial 3D scene understanding is essential. To this end, we propose a generative pipeline performing on a sparse grid-based neural scene representation to complete unobserved scene parts via a learned distribution of scenes in a 2.5D-3D-2.5D manner. We process encoded image features in 3D space with a geometry completion network and a subsequent texture inpainting network to extrapolate the missing area. Photorealistic image sequences can be finally obtained via consistency-relevant differentiable rendering. Comprehensive experiments show that the graphical outputs of our method outperform the state of the art, especially within unobserved scene parts.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 27 of its 30 references that are in the map. First posted on arXiv 23 Jul 2022.

Builds on

27 of 30 references.

References · 27

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15 more
  1. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 689
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  5. 2021ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Datacited by 454
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  14. 2020SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scanscited by 34
  15. 2020Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Imagecited by 25

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Citing papers per year: 2022: 3; 2023: 3; 2024: 1; 2025: 2.

9 papers.