mlsanityData to 7 Oct 2026

DPDist : Comparing Point Clouds Using Deep Point Cloud Distance

arXiv 2004.11784 ↗First posted 24 Apr 2020cs.CVcited by 22

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Abstract (arXiv)

We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud and the estimated surface from which the other point cloud is sampled. The surface is estimated locally and efficiently using the 3D modified Fisher vector representation. The local representation reduces the complexity of the surface, enabling efficient and effective learning, which generalizes well between object categories. We test the proposed distance in challenging tasks, such as similar object comparison and registration, and show that it provides significant improvements over commonly used distances such as Chamfer distance, Earth mover's distance, and others.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Apr 2020.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 4; 2024: 6; 2025: 2; 2026: 1.

22 papers.

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  1. 2021Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completioncited by 111
  2. 2021DiGS : Divergence guided shape implicit neural representation for unoriented point cloudscited by 76
  3. 2021MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancycited by 27
  4. 2024Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Surveycited by 9
  5. 2021Spatio-temporal Graph-RNN for Point Cloud Predictioncited by 5
  6. 2024DDM: A Metric for Comparing 3D Shapes Using Directional Distance Fieldscited by 5
  7. 2024Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distancecited by 4
  8. 2023Synthesizing realistic sand assemblies with denoising diffusion in latent spacecited by 3
  9. 2024Efficient approximation of Earth Mover's Distance Based on Nearest Neighbor Searchcited by 3
  10. 2022Scale-Invariant Fast Functional Registrationcited by 2
  11. 2023Unleash the Potential of 3D Point Cloud Modeling with A Calibrated Local Geometry-driven Distance Metriccited by 2
  12. 2023Irregular Change Detection in Sparse Bi-Temporal Point Clouds using Learned Place Recognition Descriptors and Point-to-Voxel Comparisoncited by 2
10 more
  1. 2023DeepEMD: A Transformer-based Fast Estimation of the Earth Mover's Distancecited by 2
  2. 2021DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registrationcited by 1
  3. 2022CD2: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distancecited by 1
  4. 2024Collision Avoidance Metric for 3D Camera Evaluationcited by 1
  5. 20253D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluationcited by 1
  6. 2025Point Cloud Compression and Objective Quality Assessment: A Surveycited by 1
  7. 2022An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measurecited by 0
  8. 20223DLatNav: Navigating Generative Latent Spaces for Semantic-Aware 3D Object Manipulationcited by 0
  9. 2024Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractorcited by 0
  10. 2026On the Structural Failure of Chamfer Distance in 3D Shape Optimizationcited by 0