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DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization
Contact: Rui Wang, Prof. Daniel Cremers
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Abstract
For relocalization in large-scale point clouds, we propose the first approach that unifies global place recognition and local 6DoF pose refinement. To this end, we design a Siamese network that jointly learns 3D local feature detection and description directly from raw 3D points. It integrates FlexConv and Squeeze-and-Excitation (SE) to assure that the learned local descriptor captures multi-level geometric information and channel-wise relations. For detecting 3D keypoints we predict the discriminativeness of the local descriptors in an unsupervised manner. We generate the global descriptor by directly aggregating the learned local descriptors with an effective attention mechanism. In this way, local and global 3D descriptors are inferred in one single forward pass. Experiments on various benchmarks demonstrate that our method achieves competitive results for both global point cloud retrieval and for local point cloud registration in comparison to state-of-the-art approaches. To validate the generalizability and robustness of our 3D keypoints, we demonstrate that our method also performs favorably without fine-tuning on registration of point clouds that were generated by a visual SLAM system.
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Conference and Workshop Papers
2021
[] SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition , In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. ([arxiv])
Oral Presentation
2020
[] DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization , In European Conference on Computer Vision (ECCV), 2020. ([project page][code][supplementary][arxiv])
Spotlight Presentation
2017
[] Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras , In International Conference on Computer Vision (ICCV), 2017. ([supplementary][video][arxiv][project])