Abdelhak Saouli
ABSTRACT
The human brain is constantly solving enormous and challenging optimization problems in vision. Due to the formidable meta-heuristics engine our brain equipped with, in addition to the widespread associative inputs from all other senses that act as the perfect initial guesses for a heuristic algorithm, the produced solutions are guaranteed to be optimal. By the same token, we address the problem of computing the depth and normal maps of a given scene under a natural but unknown illumination utilizing particle swarm optimization (PSO) to maximize a sophisticated photo-consistency function. For each output pixel, the swarm is initialized with good guesses starting with SIFT features as well as the optimal solution (depth, normal) found previously during the optimization. This leads to significantly better accuracy and robustness to textureless or quite specular surfaces.
Supplemental Material
Available for Download
Supplemental files.
- Yasutaka Furukawa and Jean Ponce. 2010. Accurate, Dense, and Robust Multiview Stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32, 8 (2010), 1362--1376. Google Scholar
Digital Library
- Fabian Langguth, Kalyan Sunkavalli, Sunil Hadap, and Michael Goesele. 2016. Shading-Aware Multi-view Stereo. In Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part III. 469--485.Google Scholar
- Steven M. Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. 2006. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 17--22 June 2006, New York, NY, USA. 519--528. Google ScholarDigital Library
- Enliang Zheng, Enrique Dunn, Vladimir Jojic, and Jan-Michael Frahm. 2014. Patch-Match Based Joint View Selection and Depthmap Estimation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23--28, 2014. 1510--1517. Google ScholarDigital Library
Index Terms
Towards a stochastic depth maps estimation for textureless and quite specular surfaces
Recommendations
Modelling and accuracy estimation of a new omnidirectional depth computation sensor
Depth computation is an attractive feature in computer vision. The use of traditional perspective cameras for panoramic perception requires several images, most likely implying the use of several cameras or of a sensor with mobile elements. Moreover, ...
EMVS: Event-Based Multi-View Stereo--3D Reconstruction with an Event Camera in Real-Time
Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in ...
Towards Unrestrained Depth Inference with Coherent Occlusion Filling
Traditional depth estimation methods typically exploit the effect of either the variations in internal parameters such as aperture and focus (as in depth from defocus), or variations in extrinsic parameters such as position and orientation of the camera ...
Comments