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Neural Radiance Fields from Sparse input} (NeRF-S) have shown great potentialin synthesizing novel views with a limited number of observed viewpoints.However, due to the inherent limitations of sparse inputs and the gap betweennon-adjacent views, rendering results often suffer from over-fitting and foggysurfaces, a phenomenon we refer to as "CONFUSION" during volume rendering. Inthis paper, we analyze the root cause of this confusion and attribute it to twofundamental questions: "WHERE" and "HOW". To this end, we present a novellearning framework, WaH-NeRF, which effectively mitigates confusion by tacklingthe following challenges: (i)"WHERE" to Sample? in NeRF-S -- we introduce aDeformable Sampling strategy and a Weight-based Mutual Information Loss toaddress sample-position confusion arising from the limited number ofviewpoints; and (ii) "HOW" to Predict? in NeRF-S -- we propose aSemi-Supervised NeRF learning Paradigm based on pose perturbation and aPixel-Patch Correspondence Loss to alleviate prediction confusion caused by thedisparity between training and testing viewpoints. By integrating our proposedmodules and loss functions, WaH-NeRF outperforms previous methods under theNeRF-S setting. Code is available https://github.com/y-99/WaH-NeRF.

Where and How: Mitigating Confusion in Neural Radiance Fields from Sparse Inputs