Pixelwise view selection for unstructured multi-view stereo

JL Schönberger, E Zheng, JM Frahm… - Computer Vision–ECCV …, 2016 - Springer
Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016Springer
This work presents a Multi-View Stereo system for robust and efficient dense modeling from
unstructured image collections. Our core contributions are the joint estimation of depth and
normal information, pixelwise view selection using photometric and geometric priors, and a
multi-view geometric consistency term for the simultaneous refinement and image-based
depth and normal fusion. Experiments on benchmarks and large-scale Internet photo
collections demonstrate state-of-the-art performance in terms of accuracy, completeness …
Abstract
This work presents a Multi-View Stereo system for robust and efficient dense modeling from unstructured image collections. Our core contributions are the joint estimation of depth and normal information, pixelwise view selection using photometric and geometric priors, and a multi-view geometric consistency term for the simultaneous refinement and image-based depth and normal fusion. Experiments on benchmarks and large-scale Internet photo collections demonstrate state-of-the-art performance in terms of accuracy, completeness, and efficiency.
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