Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data
Nilesh Kulkarni Linyi Jin Justin Johnson David F. Fouhey
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.