Flexible-weighted Chamfer Distance: Enhanced Objective Function for Point Cloud Completion

• URL: http://arxiv.org/abs/2505.14218v1
• Date: Tue, 20 May 2025 11:26:05 GMT
• Title: Flexible-weighted Chamfer Distance: Enhanced Objective Function for Point Cloud Completion
• Authors: Jie Li, Shengwei Tian, Long Yu, Xin Ning,
• Abstract summary: Chamfer Distance (CD) comprises two components that can evaluate the global distribution and local performance of generated point clouds.<n>We propose a Flexible-Weighted Chamfer Distance (FCD) to guide point cloud generation.<n>FCD assigns a higher weight to the global distribution component of CD and incorporates a flexible weighting strategy to adjust the balance between the two components.
• Score: 14.658272672392247
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chamfer Distance (CD) comprises two components that can evaluate the global distribution and local performance of generated point clouds, making it widely utilized as a similarity measure between generated and target point clouds in point cloud completion tasks. Additionally, CD's computational efficiency has led to its frequent application as an objective function for guiding point cloud generation. However, using CD directly as an objective function with fixed equal weights for its two components can often result in seemingly high overall performance (i.e., low CD score), while failing to achieve a good global distribution. This is typically reflected in high Earth Mover's Distance (EMD) and Decomposed Chamfer Distance (DCD) scores, alongside poor human assessments. To address this issue, we propose a Flexible-Weighted Chamfer Distance (FCD) to guide point cloud generation. FCD assigns a higher weight to the global distribution component of CD and incorporates a flexible weighting strategy to adjust the balance between the two components, aiming to improve global distribution while maintaining robust overall performance. Experimental results on two state-of-the-art networks demonstrate that our method achieves superior results across multiple evaluation metrics, including CD, EMD, DCD, and F-Score, as well as in human evaluations.

Related papers

• PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport [56.56430888985025]
  We propose a generative adversarial network for point cloud quality enhancement (PCE-GAN)<n>The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit.<n>The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction.
  arXiv  Detail & Related papers  (2025-02-26T07:34:33Z)
• Hyperbolic Chamfer Distance for Point Cloud Completion and Beyond [18.802664517825132]
  Chamfer Distance (CD) is vulnerable to the presence of outliers.<n>Hyperbolic Chamfer Distance (HyperCD) is specifically designed for point cloud completion tasks.
  arXiv  Detail & Related papers  (2024-12-23T20:04:07Z)
• Multiway Point Cloud Mosaicking with Diffusion and Global Optimization [74.3802812773891]
  We introduce a novel framework for multiway point cloud mosaicking (named Wednesday) At the core of our approach is ODIN, a learned pairwise registration algorithm that identifies overlaps and refines attention scores. Tested on four diverse, large-scale datasets, our method state-of-the-art pairwise and rotation registration results by a large margin on all benchmarks.
  arXiv  Detail & Related papers  (2024-03-30T17:29:13Z)
• Point Cloud Pre-training with Diffusion Models [62.12279263217138]
  We propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif) PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-25T08:10:05Z)
• Camouflaged Object Detection via Context-aware Cross-level Fusion [10.942917945534678]
  Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. We propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features. C2F-Net is an effective COD model and outperforms state-of-the-art (SOTA) models remarkably.
  arXiv  Detail & Related papers  (2022-07-27T08:34:16Z)
• A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion [69.32451612060214]
  Real-scanned 3D point clouds are often incomplete, and it is important to recover complete point clouds for downstream applications. Most existing point cloud completion methods use Chamfer Distance (CD) loss for training. We propose a novel Point Diffusion-Refinement (PDR) paradigm for point cloud completion.
  arXiv  Detail & Related papers  (2021-12-07T06:59:06Z)
• Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion [90.26652899910019]
  Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics for measuring the similarity between two point sets. We propose a new similarity measure named Density-aware Chamfer Distance (DCD) We show that DCD pays attention to both the overall structure and local details and provides a more reliable evaluation even when CD and contradict each other.
  arXiv  Detail & Related papers  (2021-11-24T18:56:27Z)
• LiDAR-based Panoptic Segmentation via Dynamic Shifting Network [56.71765153629892]
  LiDAR-based panoptic segmentation aims to parse both objects and scenes in a unified manner. We propose the Dynamic Shifting Network (DS-Net), which serves as an effective panoptic segmentation framework in the point cloud realm. Our proposed DS-Net achieves superior accuracies over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-24T08:44:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.