{"type":"ScholarlyArticle","title":"VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs","authors":[{"name":"Jiakai Sun","slug":"jiakai-sun","given":"Jiakai","family":"Sun","affiliation":"Zhejiang University"},{"name":"Zhanjie Zhang","slug":"zhanjie-zhang","given":"Zhanjie","family":"Zhang","affiliation":"Zhejiang University"},{"name":"Jiafu Chen","slug":"jiafu-chen","given":"Jiafu","family":"Chen","affiliation":"Zhejiang University"},{"name":"Guangyuan Li","slug":"guangyuan-li","given":"Guangyuan","family":"Li","affiliation":"Zhejiang University"},{"name":"Boyan Ji","slug":"boyan-ji","given":"Boyan","family":"Ji","affiliation":"Zhejiang University"},{"name":"Lei Zhao","slug":"lei-zhao","given":"Lei","family":"Zhao","affiliation":"Zhejiang University"},{"name":"Wei Xing","slug":"wei-xing","given":"Wei","family":"Xing","affiliation":"Zhejiang University"}],"abstract":"Neural Radiance Fields (NeRF) has shown great success in novel view synthesis due to its state-of-the-art quality and flexibility. However, NeRF requires dense input views (tens to hundreds) and a long training time (hours to days) for a single scene to generate high-fidelity images. Although using the voxel grids to represent the radiance field can significantly accelerate the optimization process, we observe that for sparse inputs, the voxel grids are more prone to overfitting to the training views and will have holes and floaters, which leads to artifacts. In this paper, we propose VGOS, an approach for fast (3-5 minutes) radiance field reconstruction from sparse inputs (3-10 views) to address these issues. To improve the performance of voxel-based radiance field in sparse input scenarios, we propose two methods: (a) We introduce an incremental voxel training strategy, which prevents overfitting by suppressing the optimization of peripheral voxels in the early stage of reconstruction. (b) We use several regularization techniques to smooth the voxels, which avoids degenerate solutions. Experiments demonstrate that VGOS achieves state-of-the-art performance for sparse inputs with super-fast convergence. Code will be available at https://github.com/SJoJoK/VGOS.","doi":"10.24963/ijcai.2023/157","url":"https://ijcai.org/proceedings/2023/157","pdf":"https://ijcai.org/proceedings/2023/0157.pdf","markdown":"https://ijcai.org/proceedings/2023/157.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/157","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":157,"pages":{"first":1414,"last":1422},"keywords":["Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: 3D computer vision","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Barron et al., 2021] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. 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