AI RESEARCH
The Less You Depend, The More You Learn: Synthesizing Novel Views from Sparse, Unposed Images with Minimal 3D Knowledge
arXiv CS.CV
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ArXi:2506.09885v2 Announce Type: replace Recent advances in feed-forward Novel View Synthesis (NVS) have led to a divergence between two design philosophies: bias-driven methods, which rely on explicit 3D knowledge, such as handcrafted 3D representations (e.g., NeRF and 3DGS) and camera poses annotated by Structure-from-Motion algorithms, and data-centric methods, which learn to understand 3D structure implicitly from large-scale imagery data.