# DPDist : Comparing Point Clouds Using Deep Point Cloud Distance

arXiv: https://arxiv.org/abs/2004.11784 · HTML: https://mlsanity.com/paper/2004.11784/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 22 of 22)

- [Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion](https://mlsanity.com/paper/2111.12702/) (2021)
- [DiGS : Divergence guided shape implicit neural representation for unoriented point clouds](https://mlsanity.com/paper/2106.10811/) (2021)
- [MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy](https://mlsanity.com/paper/2103.02850/) (2021)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [Spatio-temporal Graph-RNN for Point Cloud Prediction](https://mlsanity.com/paper/2102.07482/) (2021)
- [DDM: A Metric for Comparing 3D Shapes Using Directional Distance Fields](https://mlsanity.com/paper/2401.09736/) (2024)
- [Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance](https://mlsanity.com/paper/2409.06171/) (2024)
- [Synthesizing realistic sand assemblies with denoising diffusion in latent space](https://mlsanity.com/paper/2306.04411/) (2023)
- [Efficient approximation of Earth Mover's Distance Based on Nearest Neighbor Search](https://mlsanity.com/paper/2401.07378/) (2024)
- [Scale-Invariant Fast Functional Registration](https://mlsanity.com/paper/2209.12763/) (2022)
- [Unleash the Potential of 3D Point Cloud Modeling with A Calibrated Local Geometry-driven Distance Metric](https://mlsanity.com/paper/2306.00552/) (2023)
- [Irregular Change Detection in Sparse Bi-Temporal Point Clouds using Learned Place Recognition Descriptors and Point-to-Voxel Comparison](https://mlsanity.com/paper/2306.15416/) (2023)
- [DeepEMD: A Transformer-based Fast Estimation of the Earth Mover's Distance](https://mlsanity.com/paper/2311.09998/) (2023)
- [DeepUME: Learning the Universal Manifold Embedding for Robust Point Cloud Registration](https://mlsanity.com/paper/2112.09938/) (2021)
- [CD2: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance](https://mlsanity.com/paper/2206.00447/) (2022)
- [Collision Avoidance Metric for 3D Camera Evaluation](https://mlsanity.com/paper/2405.09755/) (2024)
- [3D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluation](https://mlsanity.com/paper/2505.18025/) (2025)
- [Point Cloud Compression and Objective Quality Assessment: A Survey](https://mlsanity.com/paper/2506.22902/) (2025)
- [An Interpretable Joint Nonnegative Matrix Factorization-Based Point Cloud Distance Measure](https://mlsanity.com/paper/2207.05112/) (2022)
- [3DLatNav: Navigating Generative Latent Spaces for Semantic-Aware 3D Object Manipulation](https://mlsanity.com/paper/2211.09770/) (2022)
- [Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor](https://mlsanity.com/paper/2410.09861/) (2024)
- [On the Structural Failure of Chamfer Distance in 3D Shape Optimization](https://mlsanity.com/paper/2603.09925/) (2026)

## 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.