Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model 

Jingyu Yang, Xinchen Ye, Kun Li, Chunping Hou, and Yao Wang

http://cs.tju.edu.cn/faculty/likun/projects/depth_recovery/index.htm 

Abstract

This paper proposes an adaptive color-guided auto-regressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We observe and verify that the AR model tightly fits depth maps of generic scenes. The depth recovery task is formulated into a minimization of AR prediction errors subject to measurement consistency. The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the nonlocal similarity in the accompanied high quality color image. We analyze the stability of our method from a linear system point of view, and design a parameter adaptation scheme to achieve stable and accurate depth recovery. Quantitative and qualitative results show that our method outperforms four state-of-the-art schemes. Being able to handle various types of depth degradations, the proposed method is versatile for mainstream depth sensors, ToF camera and Kinect, as demonstrated by experiments on real systems.

Keywords: Depth recovery (upsampling, inpainting, denoising), autoregressive model, RGB-D camera (ToF camera, Kinect)

Downloads

Datasets: Various datasets can be downloaded via the links attached to the following tables and figures.

Source code: download here

The degraded datasets in Table 1 can be obtained by downsampling the original high-resolution Middlebury datasets.

Publications

[1] Jingyu Yang, Xinchen Ye, Kun Li, Chunping Hou, Yao Wang, “Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model”, IEEE Transactions on Image Processing, vol. 23, no. 8, pp. 3443-3458, 2014. [pdf][bib]
[2] Jingyu Yang, Xinchen Ye, Kun Li, and Chunping Hou, “Depth recovery using an adaptive color-guided auto-regressive model”, European Conference on Computer Vision (ECCV), October 7-13, 2012, Firenze, Italy. [pdf] [bib]