Geometric Image Processing Lab

RGBD-Fusion: Real-Time High Precision Depth Recovery

CVPR 2015

Roy Or El1     Guy Rosman2     Aaron Wetzler1     Ron Kimmel1     Alfred M. Bruckstein1

1Technion Israel Institute of Technology

2Computer Science and Artificial Intelligence Lab, MIT



The popularity of low-cost RGB-D scanners is increasing on a daily basis. Nevertheless, existing scanners often cannot capture subtle details in the environment. We present a novel method to enhance the depth map by fusing the intensity and depth information to create more detailed range profiles. The lighting model we use can handle natural scene illumination. It is integrated in a shape from shading like technique to improve the visual fidelity of the reconstructed object. Unlike previous efforts in this domain, the detailed geometry is calculated directly, without the need to explicitly find and integrate surface normals. In addition, the proposed method operates four orders of magnitude faster than the state of the art. Qualitative and quantitative visual and statistical evidence support the improvement in the depth obtained by the suggested method.





1.    RGBD-Fusion: Real-Time High Precision Depth Recovery. Roy Or-El, Guy Rosman, Aaron Wetzler, Ron Kimmel, Alfred M. Bruckstein. In proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [pdf] [extended abstract] [bibtex]


A Matlab demo of our method for evaluation and comparisons can be downloaded here: [code]