EADReg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model for Outdoor Point Cloud Registration

• URL: http://arxiv.org/abs/2411.15271v1
• Date: Fri, 22 Nov 2024 13:03:54 GMT
• Title: EADReg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model for Outdoor Point Cloud Registration
• Authors: Linrui Gong, Jiuming Liu, Junyi Ma, Lihao Liu, Yaonan Wang, Hesheng Wang,
• Abstract summary: We propose a novel framework named EADReg for efficient and robust registration of LiDAR point clouds. We employ a Bi-directional Gaussian Mixture Model (BGMM) to reject outlier points and obtain purified point cloud pairs. In the fine stage, we treat diffusion-based PCR as an autoregressive process to generate robust point correspondences.
• Score: 22.084204833166982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have shown the great potential in the point cloud registration (PCR) task, especially for enhancing the robustness to challenging cases. However, existing diffusion-based PCR methods primarily focus on instance-level scenarios and struggle with outdoor LiDAR points, where the sparsity, irregularity, and huge point scale inherent in LiDAR points pose challenges to establishing dense global point-to-point correspondences. To address this issue, we propose a novel framework named EADReg for efficient and robust registration of LiDAR point clouds based on autoregressive diffusion models. EADReg follows a coarse-to-fine registration paradigm. In the coarse stage, we employ a Bi-directional Gaussian Mixture Model (BGMM) to reject outlier points and obtain purified point cloud pairs. BGMM establishes correspondences between the Gaussian Mixture Models (GMMs) from the source and target frames, enabling reliable coarse registration based on filtered features and geometric information. In the fine stage, we treat diffusion-based PCR as an autoregressive process to generate robust point correspondences, which are then iteratively refined on upper layers. Despite common criticisms of diffusion-based methods regarding inference speed, EADReg achieves runtime comparable to convolutional-based methods. Extensive experiments on the KITTI and NuScenes benchmark datasets highlight the state-of-the-art performance of our proposed method. Codes will be released upon publication.

Related papers

• A Moment Matching-Based Method for Sparse and Noisy Point Cloud Registration [8.121132773789652]
  We propose a registration framework based on moment matching.<n>Experiments on synthetic and real-world datasets show that our approach achieves higher accuracy and robustness than existing methods.<n>The proposed method significantly improves the localization performance and achieves results comparable to LiDAR-based systems.
  arXiv  Detail & Related papers  (2025-08-04T08:31:53Z)
• Navigating Sparse Molecular Data with Stein Diffusion Guidance [48.21071466968102]
  optimal control (SOC) has emerged as a principled framework for fine-tuning diffusion models.<n>A class of training-free approaches has been developed that guides diffusion models using off-the-shelf classifiers on predicted clean samples.<n>We propose a novel training-free guidance framework based on a surrogate optimal control objective.
  arXiv  Detail & Related papers  (2025-07-07T21:14:27Z)
• Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering [18.543769006014383]
  Diffusion models often exhibit inconsistent sample quality due to variations inherent in their sampling trajectories.<n>We introduce CFG-Rejection, an efficient, plug-and-play strategy that filters low-quality samples at an early stage of the denoising process.<n>We validate the effectiveness of CFG-Rejection in image generation through extensive experiments.
  arXiv  Detail & Related papers  (2025-05-29T11:08:24Z)
• Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning [9.025671446527694]
  Reinforcement learning from human feedback (RLHF) has become a crucial step in building reliable generative AI models. This study is to develop a disciplined approach to fine-tune diffusion models using continuous-time RL.
  arXiv  Detail & Related papers  (2025-02-03T20:50:05Z)
• A Consistency-Aware Spot-Guided Transformer for Versatile and Hierarchical Point Cloud Registration [9.609585217048664]
  We develop a consistency-aware spot-guided Transformer (CAST) CAST incorporates a spot-guided cross-attention module to avoid interfering with irrelevant areas. A lightweight fine matching module for both sparse keypoints and dense features can estimate the transformation accurately.
  arXiv  Detail & Related papers  (2024-10-14T08:48:25Z)
• Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction [88.65168366064061]
  We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference. Our framework leads to a family of three novel objectives that are all simulation-free, and thus scalable. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
  arXiv  Detail & Related papers  (2024-10-10T17:18:30Z)
• BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution [52.47005445345593]
  BlindDiff is a DM-based blind SR method to tackle the blind degradation settings in SISR. BlindDiff seamlessly integrates the MAP-based optimization into DMs. Experiments on both synthetic and real-world datasets show that BlindDiff achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-03-15T11:21:34Z)
• MEDPNet: Achieving High-Precision Adaptive Registration for Complex Die Castings [10.504847830252254]
  This paper proposes a high-precision adaptive registration method called Multiscale Efficient Deep Closest Point (MEDPNet) The MEDPNet method performs coarse die-casting point cloud data registration using the Efficient-DCP method, followed by precision registration using the Multiscale feature fusion dual-channel registration (MDR) method. Our proposed method demonstrates excellent performance compared to state-of-the-art geometric and learning-based registration methods when applied to complex die-casting point cloud data.
  arXiv  Detail & Related papers  (2024-03-15T03:42:38Z)
• DiffusionPCR: Diffusion Models for Robust Multi-Step Point Cloud Registration [73.37538551605712]
  Point Cloud Registration (PCR) estimates the relative rigid transformation between two point clouds. We propose formulating PCR as a denoising diffusion probabilistic process, mapping noisy transformations to the ground truth. Our experiments showcase the effectiveness of our DiffusionPCR, yielding state-of-the-art registration recall rates (95.3%/81.6%) on 3D and 3DLoMatch.
  arXiv  Detail & Related papers  (2023-12-05T18:59:41Z)
• Insights into Closed-form IPM-GAN Discriminator Guidance for Diffusion Modeling [11.68361062474064]
  We propose a theoretical framework to analyze the effect of the GAN discriminator on Langevin-based sampling.<n>We show that the proposed approach can be combined with existing accelerated-diffusion techniques to improve latent-space image generation.
  arXiv  Detail & Related papers  (2023-06-02T16:24:07Z)
• Reflected Diffusion Models [93.26107023470979]
  We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data. Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
  arXiv  Detail & Related papers  (2023-04-10T17:54:38Z)
• Controllable Mesh Generation Through Sparse Latent Point Diffusion Models [105.83595545314334]
  We design a novel sparse latent point diffusion model for mesh generation. Our key insight is to regard point clouds as an intermediate representation of meshes, and model the distribution of point clouds instead. Our proposed sparse latent point diffusion model achieves superior performance in terms of generation quality and controllability.
  arXiv  Detail & Related papers  (2023-03-14T14:25:29Z)
• Overlap-guided Gaussian Mixture Models for Point Cloud Registration [61.250516170418784]
  Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. This paper proposes a novel overlap-guided probabilistic registration approach that computes the optimal transformation from matched Gaussian Mixture Model (GMM) parameters.
  arXiv  Detail & Related papers  (2022-10-17T08:02:33Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)
• LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration [1.8876415010297891]
  We propose a novel method called CPD with Local Surface Geometry (LSG-CPD) for rigid point cloud registration. Our method adaptively adds different levels of point-to-plane penalization on top of the point-to-point penalization based on the flatness of the local surface. It is significantly faster than modern implementations of CPD.
  arXiv  Detail & Related papers  (2021-03-28T03:46:41Z)

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.