Where and How: Mitigating Confusion in Neural Radiance Fields from
  Sparse Inputs
Yanqi Bao Yuxin Li Jing Huo Tianyu Ding Xinyue Liang Wenbin Li Yang Gao
Neural Radiance Fields from Sparse input} (NeRF-S) have shown great potential
in synthesizing novel views with a limited number of observed viewpoints.
However, due to the inherent limitations of sparse inputs and the gap between
non-adjacent views, rendering results often suffer from over-fitting and foggy
surfaces, a phenomenon we refer to as "CONFUSION" during volume rendering. In
this paper, we analyze the root cause of this confusion and attribute it to two
fundamental questions: "WHERE" and "HOW". To this end, we present a novel
learning framework, WaH-NeRF, which effectively mitigates confusion by tackling
the following challenges: (i)"WHERE" to Sample? in NeRF-S -- we introduce a
Deformable Sampling strategy and a Weight-based Mutual Information Loss to
address sample-position confusion arising from the limited number of
viewpoints; and (ii) "HOW" to Predict? in NeRF-S -- we propose a
Semi-Supervised NeRF learning Paradigm based on pose perturbation and a
Pixel-Patch Correspondence Loss to alleviate prediction confusion caused by the
disparity between training and testing viewpoints. By integrating our proposed
modules and loss functions, WaH-NeRF outperforms previous methods under the
NeRF-S setting. Code is available https://github.com/bbbbby-99/WaH-NeRF.