VEGS: View Extrapolation of Urban Scenes in 3D Gaussian Splatting using Learned Priors

Autor:	Hwang, Sungwon, Kim, Min-Jung, Kang, Taewoong, Kang, Jayeon, Choo, Jaegul
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Neural rendering-based urban scene reconstruction methods commonly rely on images collected from driving vehicles with cameras facing and moving forward. Although these methods can successfully synthesize from views similar to training camera trajectory, directing the novel view outside the training camera distribution does not guarantee on-par performance. In this paper, we tackle the Extrapolated View Synthesis (EVS) problem by evaluating the reconstructions on views such as looking left, right or downwards with respect to training camera distributions. To improve rendering quality for EVS, we initialize our model by constructing dense LiDAR map, and propose to leverage prior scene knowledge such as surface normal estimator and large-scale diffusion model. Qualitative and quantitative comparisons demonstrate the effectiveness of our methods on EVS. To the best of our knowledge, we are the first to address the EVS problem in urban scene reconstruction. Link to our project page: https://vegs3d.github.io/. Comment: The first two authors contributed equally. Project Page: https://vegs3d.github.io/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.02945 Zobrazit plný text záznamu View this record from Arxiv