Large Scale Photometric Bundle Adjustment

• URL: http://arxiv.org/abs/2008.11762v2
• Date: Thu, 10 Sep 2020 20:31:47 GMT
• Title: Large Scale Photometric Bundle Adjustment
• Authors: Oliver J. Woodford, Edward Rosten
• Abstract summary: offline 3-d reconstruction from internet images has not yet benefited from a joint, photometric optimization over dense geometry and camera parameters. This work presents a framework for jointly optimizing millions of scene points and hundreds of camera poses and intrinsics. The improvement in metric reconstruction accuracy that it confers over feature-based bundle adjustment is demonstrated on the large-scale Tanks & Temples benchmark.
• Score: 9.184692492399686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Direct methods have shown promise on visual odometry and SLAM, leading to greater accuracy and robustness over feature-based methods. However, offline 3-d reconstruction from internet images has not yet benefited from a joint, photometric optimization over dense geometry and camera parameters. Issues such as the lack of brightness constancy, and the sheer volume of data, make this a more challenging task. This work presents a framework for jointly optimizing millions of scene points and hundreds of camera poses and intrinsics, using a photometric cost that is invariant to local lighting changes. The improvement in metric reconstruction accuracy that it confers over feature-based bundle adjustment is demonstrated on the large-scale Tanks & Temples benchmark. We further demonstrate qualitative reconstruction improvements on an internet photo collection, with challenging diversity in lighting and camera intrinsics.

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