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|Abstract:||Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3DMatch, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3DMatch consistently outperforms other state-of-the-art approaches by a significant margin. Code, data, benchmarks, and pre-trained models are available online at http://3dmatch.cs.princeton.edu.|
|Citation:||Zeng, Andy, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and Thomas Funkhouser. "3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions." In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017): pp. 199-208. doi:10.1109/CVPR.2017.29|
|Pages:||199 - 208|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|
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