{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/novel-view-synthesis-for-large-scale-scene","title":"Novel View Synthesis for Large-scale Scene using Adversarial Loss","arxiv_id":"1802.07064","date":"2018-02-20","proceeding":null,"authors":["Xiaochuan Yin","Henglai Wei","Penghong Lin","Xiangwei Wang","Qijun Chen"],"abstract":"Novel view synthesis aims to synthesize new images from different viewpoints\nof given images. Most of previous works focus on generating novel views of\ncertain objects with a fixed background. However, for some applications, such\nas virtual reality or robotic manipulations, large changes in background may\noccur due to the egomotion of the camera. Generated images of a large-scale\nenvironment from novel views may be distorted if the structure of the\nenvironment is not considered. In this work, we propose a novel fully\nconvolutional network, that can take advantage of the structural information\nexplicitly by incorporating the inverse depth features. The inverse depth\nfeatures are obtained from CNNs trained with sparse labeled depth values. This\nframework can easily fuse multiple images from different viewpoints. To fill\nthe missing textures in the generated image, adversarial loss is applied, which\ncan also improve the overall image quality. Our method is evaluated on the\nKITTI dataset. The results show that our method can generate novel views of\nlarge-scale scene without distortion. The effectiveness of our approach is\ndemonstrated through qualitative and quantitative evaluation.","url_abs":"http://arxiv.org/abs/1802.07064v1","url_pdf":"http://arxiv.org/pdf/1802.07064v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"novel-view-synthesis-for-large-scale-scene","repo_url":"https://github.com/tinghuiz/appearance-flow","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"novel-view-synthesis","task_name":"Novel View Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}