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We present Depth-aware Image-based NEural Radiance fields (DINER). Given asparse set of RGB input views, we predict depth and feature maps to guide thereconstruction of a volumetric scene representation that allows us to render 3Dobjects under novel views. Specifically, we propose novel techniques toincorporate depth information into feature fusion and efficient scene sampling.In comparison to the previous state of the art, DINER achieves higher synthesisquality and can process input views with greater disparity. This allows us tocapture scenes more completely without changing capturing hardware requirementsand ultimately enables larger viewpoint changes during novel view synthesis. Weevaluate our method by synthesizing novel views, both for human heads and forgeneral objects, and observe significantly improved qualitative results andincreased perceptual metrics compared to the previous state of the art. Thecode is publicly available for research purposes.

DINER: Depth-aware Image-based NEural Radiance fields