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|Abstract:||We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction. The core of our method is a deep convolutional neural network that takes in RGB, depth, and normal information of a planar patch in an image and outputs a descriptor that can be used to find coplanar patches from other images. We train the network on 10 million triplets of coplanar and non-coplanar patches, and evaluate on a new coplanarity benchmark created from commodity RGB-D scans. Experiments show that our learned descriptor outperforms alternatives extended for this new task by a significant margin. In addition, we demonstrate the benefits of coplanarity matching in a robust RGBD reconstruction formulation. We find that coplanarity constraints detected with our method are sufficient to get reconstruction results comparable to state-of-the-art frameworks on most scenes, but outperform other methods on established benchmarks when combined with traditional keypoint matching.|
|Citation:||Shi, Yifei, Kai Xu, Matthias Nießner, Szymon Rusinkiewicz, and Thomas Funkhouser. "PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction." In European Conference on Computer Vision (2018): pp. 767-784. doi:10.1007/978-3-030-01237-3_46|
|Pages:||767 - 784|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||European Conference on Computer Vision|
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