Novel View Synthesis Under Large-Deviation Viewpoint for Autonomous Driving

Novel view synthesis is a critical task in autonomous driving. Although 3D Gaussian Splatting (3D-GS) has shown success in generating novel views, it faces challenges in maintaining high-quality rendering when viewpoints deviate significantly from the training set. This difficulty primarily stems from complex lighting conditions and geometric inconsistencies in texture-less regions. To address these issues, we propose an attention-based illumination model that leverages light fields from neighboring views, enhancing the realism of synthesized images. Additionally, we propose a geometry optimization method using planar homography to improve geometric consistency in texture-less regions. Our experiments demonstrate substantial improvements in synthesis quality for large-deviation viewpoints, validating the effectiveness of our approach.

Authors

• Xin Ma
• Jiguang Zhang MAIS, Institute of Automation, Chinese Academy of Sciences
• Peng Lu Beijing University of Posts and Telecommunications
• Shibiao Xu School of Artificial Intelligence, Beijing University of Posts and Telecommunications
• Chengwei Pan Beihang University Zhongguancun Laboratory

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