Multi View Image
Multi-view image processing focuses on leveraging information from multiple viewpoints to achieve more robust and complete scene understanding than single-view methods. Current research heavily emphasizes using this data for 3D reconstruction, often employing neural radiance fields (NeRFs), diffusion models, and Gaussian splatting techniques to generate novel views, create 3D models from 2D images, and improve tasks like object detection and image editing. These advancements have significant implications for various fields, including robotics, autonomous driving, and 3D modeling, by enabling more accurate and efficient scene representation and manipulation. The development of robust and generalizable methods for handling occlusions, varying weather conditions, and limited data remains a key challenge.
Papers
Customizing Text-to-Image Diffusion with Object Viewpoint Control
Nupur Kumari, Grace Su, Richard Zhang, Taesung Park, Eli Shechtman, Jun-Yan Zhu
Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models
Shouwei Ruan, Yinpeng Dong, Hanqing Liu, Yao Huang, Hang Su, Xingxing Wei