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3D reconstruction from a single 2D image was extensively covered in theliterature but relies on depth supervision at training time, which limits itsapplicability. To relax the dependence to depth we propose SceneRF, aself-supervised monocular scene reconstruction method using only posed imagesequences for training. Fueled by the recent progress in neural radiance fields(NeRF) we optimize a radiance field though with explicit depth optimization anda novel probabilistic sampling strategy to efficiently handle large scenes. Atinference, a single input image suffices to hallucinate novel depth views whichare fused together to obtain 3D scene reconstruction. Thorough experimentsdemonstrate that we outperform all baselines for novel depth views synthesisand scene reconstruction, on indoor BundleFusion and outdoor SemanticKITTI.Code is available at https://astra-vision.github.io/SceneRF .

SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields