[2306.08671] Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2306.08671 (cs)
[Submitted on 14 Jun 2023]

Title:Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data

Authors:Nilesh Kulkarni, Linyi Jin, Justin Johnson, David F. Fouhey
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Abstract: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.
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Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2306.08671 [cs.CV]
  (or arXiv:2306.08671v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2306.08671

Submission history

From: Nilesh Kulkarni [view email]
[v1] Wed, 14 Jun 2023 17:59:36 UTC (47,943 KB)
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