UniRiT: Towards Few-Shot Non-Rigid Point Cloud Registration

• URL: http://arxiv.org/abs/2410.22909v1
• Date: Wed, 30 Oct 2024 11:06:23 GMT
• Title: UniRiT: Towards Few-Shot Non-Rigid Point Cloud Registration
• Authors: Geng Li, Haozhi Cao, Mingyang Liu, Chenxi Jiang, Jianfei Yang,
• Abstract summary: Non-rigid point cloud registration is a critical challenge in 3D scene understanding, particularly in surgical navigation. Existing methods degrade significantly since non-rigid patterns are more flexible and complicated than rigid ones. We introduce a new dataset, MedMatch3D, which consists of real human organs and exhibits high variability in sample distribution.
• Score: 15.17153502202129
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Non-rigid point cloud registration is a critical challenge in 3D scene understanding, particularly in surgical navigation. Although existing methods achieve excellent performance when trained on large-scale, high-quality datasets, these datasets are prohibitively expensive to collect and annotate, e.g., organ data in authentic medical scenarios. With insufficient training samples and data noise, existing methods degrade significantly since non-rigid patterns are more flexible and complicated than rigid ones, and the distributions across samples are more distinct, leading to higher difficulty in representation learning with few data. In this work, we aim to deal with this challenging few-shot non-rigid point cloud registration problem. Based on the observation that complex non-rigid transformation patterns can be decomposed into rigid and small non-rigid transformations, we propose a novel and effective framework, UniRiT. UniRiT adopts a two-step registration strategy that first aligns the centroids of the source and target point clouds and then refines the registration with non-rigid transformations, thereby significantly reducing the problem complexity. To validate the performance of UniRiT on real-world datasets, we introduce a new dataset, MedMatch3D, which consists of real human organs and exhibits high variability in sample distribution. We further establish a new challenging benchmark for few-shot non-rigid registration. Extensive empirical results demonstrate that UniRiT achieves state-of-the-art performance on MedMatch3D, improving the existing best approach by 94.22%.

Related papers

• Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration [2.814748676983944]
  We propose a graph neural network model embedded with a local Spherical Euclidean 3D equivariance property through SE(3) message passing based propagation. Our model is composed mainly of a descriptor module, equivariant graph layers, match similarity, and the final regression layers. Experiments conducted on the 3DMatch and KITTI datasets exhibit the compelling and robust performance of our model compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-10-08T06:48:01Z)
• Contrastive Multiple Instance Learning for Weakly Supervised Person ReID [50.04900262181093]
  We introduce Contrastive Multiple Instance Learning (CMIL), a novel framework tailored for more effective weakly supervised ReID. CMIL distinguishes itself by requiring only a single model and no pseudo labels while leveraging contrastive losses. We release the WL-MUDD dataset, an extension of the MUDD dataset featuring naturally occurring weak labels from the real-world application at PerformancePhoto.co.
  arXiv  Detail & Related papers  (2024-02-12T14:48:31Z)
• Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization [56.95046107046027]
  We propose to leverage Neural Radiance Fields (NeRF) to generate training samples for scene coordinate regression. Despite NeRF's efficiency in rendering, many of the rendered data are polluted by artifacts or only contain minimal information gain.
  arXiv  Detail & Related papers  (2023-10-10T20:11:13Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• Training on Thin Air: Improve Image Classification with Generated Data [28.96941414724037]
  Diffusion Inversion is a simple yet effective method to generate diverse, high-quality training data for image classification. Our approach captures the original data distribution and ensures data coverage by inverting images to the latent space of Stable Diffusion. We identify three key components that allow our generated images to successfully supplant the original dataset.
  arXiv  Detail & Related papers  (2023-05-24T16:33:02Z)
• Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation [78.6612285236938]
  We propose a novel DAT (textbfDual textbfAdaptive textbfTransformations) model for weakly supervised point cloud segmentation. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets.
  arXiv  Detail & Related papers  (2022-07-19T05:43:14Z)
• Learning-based Point Cloud Registration for 6D Object Pose Estimation in the Real World [55.7340077183072]
  We tackle the task of estimating the 6D pose of an object from point cloud data. Recent learning-based approaches to addressing this task have shown great success on synthetic datasets. We analyze the causes of these failures, which we trace back to the difference between the feature distributions of the source and target point clouds.
  arXiv  Detail & Related papers  (2022-03-29T07:55:04Z)
• Recurrent Multi-view Alignment Network for Unsupervised Surface Registration [79.72086524370819]
  Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data. We propose to represent the non-rigid transformation with a point-wise combination of several rigid transformations. We also introduce a differentiable loss function that measures the 3D shape similarity on the projected multi-view 2D depth images.
  arXiv  Detail & Related papers  (2020-11-24T14:22:42Z)
• Orderly Disorder in Point Cloud Domain [25.36505222529359]
  We propose a smart yet simple deep network for analysis of 3D models using orderly disorder' theory. Our method extracts the deep patterns inside a 3D object via creating a dynamic link to seek the most stable patterns. Our model alleviates the vanishing-gradient problem, strengthens dynamic link propagation and substantially reduces the number of parameters.
  arXiv  Detail & Related papers  (2020-08-21T18:18:09Z)
• A Close Look at Deep Learning with Small Data [0.0]
  We show that model complexity is a critical factor when only a few samples per class are available. We also show that even standard data augmentation can boost recognition performance by large margins.
  arXiv  Detail & Related papers  (2020-03-28T17:11:29Z)

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.