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High-dimensional Convolutional Networks for Geometric Pattern Recognition

arXiv 2005.08144 ↗First posted 17 May 2020cs.CV cs.LG stat.MLcited by 24

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Abstract (arXiv)

Many problems in science and engineering can be formulated in terms of geometric patterns in high-dimensional spaces. We present high-dimensional convolutional networks (ConvNets) for pattern recognition problems that arise in the context of geometric registration. We first study the effectiveness of convolutional networks in detecting linear subspaces in high-dimensional spaces with up to 32 dimensions: much higher dimensionality than prior applications of ConvNets. We then apply high-dimensional ConvNets to 3D registration under rigid motions and image correspondence estimation. Experiments indicate that our high-dimensional ConvNets outperform prior approaches that relied on deep networks based on global pooling operators.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 5; 2023: 2; 2024: 3; 2025: 2; 2026: 1.

24 papers.

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  1. 2021Deep Hough Voting for Robust Global Registrationcited by 53
  2. 2021P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matchingcited by 42
  3. 2022PeRFception: Perception using Radiance Fieldscited by 19
  4. 2021Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimationcited by 17
  5. 2023DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detectioncited by 16
  6. 2024DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Modelscited by 16
  7. 2021RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representationcited by 8
  8. 2022Leveraging Image Matching Toward End-to-End Relative Camera Pose Regressioncited by 6
  9. 2024Deep Learning-based Point Cloud Registration for Augmented Reality-guided Surgerycited by 6
  10. 2021Visual Correspondence Hallucinationcited by 5
  11. 2022Learning to Register Unbalanced Point Pairscited by 5
  12. 2022Phase2vec: Dynamical systems embedding with a physics-informed convolutional networkcited by 5
12 more
  1. 2021Points2Sound: From mono to binaural audio using 3D point cloud scenescited by 4
  2. 2021Dynamic Convolution for 3D Point Cloud Instance Segmentationcited by 3
  3. 2023Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learningcited by 3
  4. 2024OE3DIS: Open-Ended 3D Point Cloud Instance Segmentationcited by 3
  5. 2021(Just) A Spoonful of Refinements Helps the Registration Error Go Downcited by 2
  6. 2021Putting 3D Spatially Sparse Networks on a Dietcited by 2
  7. 2021Revisiting LiDAR Registration and Reconstruction: A Range Image Perspectivecited by 2
  8. 2021Interpolation-Aware Padding for 3D Sparse Convolutional Neural Networkscited by 1
  9. 2022Leveraging Inlier Correspondences Proportion for Point Cloud Registrationcited by 1
  10. 2026LEADER: Learning Reliable Local-to-Global Correspondences for LiDAR Relocalizationcited by 1
  11. 20253D Motion Perception of Binocular Vision Target with PID-CNNcited by 0
  12. 2025PointCNN++: Performant Convolution on Native Pointscited by 0