AS-NeRF: Learning Auxiliary Sampling for Generalizable Novel View Synthesis from Sparse Views

Jilin Tang, Lincheng Li, Xingqun Qi, Yingfeng Chen, Changjie Fan, Xin Yu

Abstract: We tackle the problem of novel view synthesis (NVS) which aims to generate realistic images at novel views. Unlike existing works that require either costly per-scene optimization or relatively dense views, we propose AS-NeRF, a neural rendering based approach that can achieve generalizable NVS from only sparse views. Considering the inherent spatial continuity of images, we design a novel sparse-attention based auxiliary sampling module (ASM). Given a 3D point on the ray, the ASM adaptively attends to a sparse set of view-specific 2D auxiliary locations around the 3D point’s original projection pixels, and dynamically computes the attention weights in a cross-attention manner. This enables our model to effectively exploit the local correlation among neighboring pixels, obtaining the enhanced features with more powerful representation. Extensive experiments show that our method outperforms the state-of-the-art on both real and synthetic data.