We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data. At test time, our system maps a previously unseen RGB image to a 3D reconstruction of a scene via implicit functions. While implicit functions for 3D reconstruction have often been tied to meshes, we show that we can train one using only a set of posed RGBD images. This setting may help 3D reconstruction unlock the sea of accelerometer+RGBD data that is coming with new phones. Our system, D2-DRDF, can match and sometimes outperform current methods that use mesh supervision and shows better robustness to sparse data.
@article{arxiv.2306.08671,
title = {Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data},
author = {Nilesh Kulkarni and Linyi Jin and Justin Johnson and David F. Fouhey},
journal= {arXiv preprint arXiv:2306.08671},
year = {2023}
}
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Project page this https://nileshkulkarni.github.io/d2drdf/