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cs.CV2024
Self-Distilled Depth Refinement with Noisy Poisson Fusion
Jiaqi Li, Yiran Wang, Jinghong Zheng +4
Depth refinement aims to infer high-resolution depth with fine-grained edges and details, refining low-resolution results of depth estimation models. The prevailing methods adopt t…
cs.CV2024
WildAvatar: Learning In-the-wild 3D Avatars from the Web
Zihao Huang, Shoukang Hu, Guangcong Wang +5
Existing research on avatar creation is typically limited to laboratory datasets, which require high costs against scalability and exhibit insufficient representation of the real w…
cs.CV2024
Geometry-aware Reconstruction and Fusion-refined Rendering for Generalizable Neural Radiance Fields
Tianqi Liu, Xinyi Ye, Min Shi +4
Generalizable NeRF aims to synthesize novel views for unseen scenes. Common practices involve constructing variance-based cost volumes for geometry reconstruction and encoding 3D d…