3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

@article{Zeng20173DMatchLL,
  title={3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions},
  author={Andy Zeng and Shuran Song and Matthias Nie{\ss}ner and Matthew Fisher and Jianxiong Xiao and Thomas A. Funkhouser},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={199-208}
}
  • Andy Zeng, S. Song, T. Funkhouser
  • Published 27 March 2016
  • Computer Science
  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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… 
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References

SHOWING 1-10 OF 53 REFERENCES
Self-Supervised Visual Descriptor Learning for Dense Correspondence
TLDR
A new approach to learning visual descriptors for dense correspondence estimation is advocated in which the power of a strong three-dimensional generative model is harnessed to automatically label correspondences in RGB-D video data.
3D ShapeNets: A deep representation for volumetric shapes
TLDR
This work proposes to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network, and shows that this 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
  • S. Song, Jianxiong Xiao
  • Computer Science
    2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
TLDR
This work proposes the first 3D Region Proposal Network (RPN) to learn objectness from geometric shapes and the first joint Object Recognition Network (ORN) to extract geometric features in 3D and color features in 2D.
Fine-to-Coarse Global Registration of RGB-D Scans
TLDR
A fine-to-coarse global registration algorithm that leverages robust registrations at finer scales to seed detection and enforcement of new correspondence and structural constraints at coarser scales is proposed.
Discriminative Learning of Deep Convolutional Feature Point Descriptors
TLDR
This paper uses Convolutional Neural Networks to learn discriminant patch representations and in particular train a Siamese network with pairs of (non-)corresponding patches to develop 128-D descriptors whose euclidean distances reflect patch similarity and can be used as a drop-in replacement for any task involving SIFT.
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels
TLDR
SUN3D, a large-scale RGB-D video database with camera pose and object labels, capturing the full 3D extent of many places is introduced, and a generalization of bundle adjustment that incorporates object-to-object correspondences is introduced.
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image. Our approach employs a regression forest that is capable of inferring
Real-time 3D reconstruction at scale using voxel hashing
TLDR
An online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure.
Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge
TLDR
This paper proposes a self-supervised method to generate a large labeled dataset without tedious manual segmentation and demonstrates that the system can reliably estimate the 6D pose of objects under a variety of scenarios.
Model globally, match locally: Efficient and robust 3D object recognition
TLDR
A novel method is proposed that creates a global model description based on oriented point pair features and matches that model locally using a fast voting scheme, which allows using much sparser object and scene point clouds, resulting in very fast performance.
...
...