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

被引：0
|
作者：
Kulkarni, Nilesh [1 ]
Jin, Linyi [1 ]
Johnson, Justin [1 ]
Fouhey, David F. [1 ]
机构：
[1] Univ Michigan, Ann Arbor, MI 48109 USA
来源：
2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2023年
关键词：
D O I：
10.1109/CVPR52729.2023.01655
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：17256 / 17265
页数：10
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