Conditional Regressive Random Forest Stereo-Based Hand Depth Recovery
Abstract
This paper introduces Conditional Regressive Random Forest (CRRF), a novel method that combines a closed-form Conditional Random Field (CRF), using learned weights, and a Regressive Random Forest (RRF) that employs adaptively selected expert trees. CRRF is used to estimate a depth image of hand given stereo RGB inputs. CRRF uses a novel superpixel-based regression framework that takes advantage of the smoothness of the hand’s depth surface. A RRF unary term adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. CRRF also includes a pair-wise term that encourages smoothness between similar adjacent superpixels. Experimental results show that CRRF can produce high quality depth maps, even using an inexpensive RGB stereo camera and produces state-of-the-art results for hand depth estimation.
Cite
Text
Basaru et al. "Conditional Regressive Random Forest Stereo-Based Hand Depth Recovery." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.78Markdown
[Basaru et al. "Conditional Regressive Random Forest Stereo-Based Hand Depth Recovery." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/basaru2017iccvw-conditional/) doi:10.1109/ICCVW.2017.78BibTeX
@inproceedings{basaru2017iccvw-conditional,
title = {{Conditional Regressive Random Forest Stereo-Based Hand Depth Recovery}},
author = {Basaru, Rilwan Remilekun and Child, Chris and Alonso, Eduardo and Slabaugh, Greg G.},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {614-622},
doi = {10.1109/ICCVW.2017.78},
url = {https://mlanthology.org/iccvw/2017/basaru2017iccvw-conditional/}
}