graph-based motion estimation

Addressing the challenge of simultaneously achieving robustness, efficiency, and generalization in global point cloud registration, this paper introduces an open-source C++ library. The proposed end-to-end pipeline integrates a lightweight Faster-PFH feature descriptor, a k-core graph-theoretic outlier pruning strategy, and robust pose solvers (e.g., RANSAC and TEASER+). Key contributions include: (i) Faster-PFH, which drastically reduces feature computation overhead while preserving discriminability; (ii) k-core pruning, lowering outlier rejection complexity from O(n²) to near-linear time; and (iii) a modular, highly extensible architecture that maintains high accuracy. Extensive experiments on standard benchmarks—including 3DMatch and KITTI—demonstrate that our method achieves 2–5× speedup over state-of-the-art robust registration approaches, with comparable registration accuracy, while supporting large-scale point clouds and cross-scenario generalization.

In large-scale Structure-from-Motion (SfM), sparse inter-view overlap and drastic viewpoint changes—especially in aerial-to-ground scenarios—lead to low cross-image feature matching density and weak geometric consistency. To address this, we propose a geometry-guided hybrid matching paradigm: (1) geometric verification is formulated as an optimization problem based on Sampson distance; (2) detector-agnostic dense matching is fused with detector-driven sparse anchor guidance, where sparse anchors constrain and enhance the geometric consistency of dense matches; and (3) multi-view geometric consistency is explicitly modeled. Our method significantly improves both matching density and accuracy, outperforming state-of-the-art approaches in extreme large-scale settings. Consequently, camera pose estimation becomes more accurate, and the reconstructed 3D point cloud achieves higher completeness and fidelity.

Establishing dense correspondences for 3D shapes in real-world scenarios is challenged by the absence of annotations, high resolution, topological distortions, and heterogeneous shape representations. This work proposes the ATM framework, which adopts a “model-then-match” paradigm by integrating pretrained vision foundation models with parametric shape priors to learn a unified shape representation in a shared parameter space from multi-view renderings. Dense correspondences are achieved in a zero-shot manner through geometric consistency constraints and spectral refinement, eliminating the need for correspondence-labeled training data. The method is inherently robust to topological noise and seamlessly handles diverse representations—including meshes, point clouds, and 3D Gaussians. It significantly outperforms existing approaches on non-isometric benchmarks, reducing correspondence errors by 73% on TOPKIDS and 37% on SMAL, while maintaining high efficiency and accuracy on wild scans with up to 200,000 vertices.

To address the unreliable pose graph construction and motion synchronization challenges in multi-view point cloud registration, this paper proposes an end-to-end absolute pose estimation paradigm. First, matching distance is introduced as a principled reliability metric for pose graph construction, replacing handcrafted loss functions with direct global pose regression. Second, the method jointly optimizes feature interaction and structural awareness by integrating local geometric distribution modeling with adaptive attention mechanisms. Fully data-driven, it eliminates iterative optimization and post-processing. Evaluated on diverse indoor and outdoor datasets, the approach achieves a 12.7% improvement in pose graph construction accuracy and reduces overall registration error by 21.3%, demonstrating significantly enhanced robustness and cross-scene generalization capability.

To address the challenge of balancing robustness and efficiency in point cloud registration under high outlier ratios, this paper proposes a geometric-overlap-guided, rotation-prior branch-and-bound (BnB) framework. Methodologically, it decouples rigid-body transformation via Chasles’ theorem into rotation-axis direction and 2D residual parameters, circumventing the quadratic complexity of conventional spatial graph construction. Rotation space is parameterized via cubic mapping over the hemisphere, enabling efficient 2D range maximum queries through interval piercing combined with a sweep-line algorithm augmented by a segment tree; only rotation is optimized via BnB. Translation and angular parameters are solved analytically. The method achieves polynomial time complexity and linear space complexity in the number of points. Extensive experiments on 3DMatch, 3DLoMatch, and KITTI demonstrate significant improvements over state-of-the-art methods, achieving both higher accuracy and real-time performance.

This study addresses the susceptibility of alternating minimization to local optima and its computational inefficiency in correspondence-free point set alignment. We propose a global optimization method based on support vectors derived from the convex hull vertices of permuted polygons. By proving a tight bound of $n(n-1)$ vertices, we resolve an open problem posed by Rote. Integrating the Procrustes-Wasserstein framework with a branch-and-bound algorithm, our approach achieves exact solutions in 2D and extends naturally to 3D. Evaluated on the MPEG-7 benchmark, the method requires only 12ms on average, achieving a 50-fold speedup over grid search while delivering superior accuracy. These improvements substantially enhance shape retrieval performance, demonstrating both theoretical rigor and practical efficiency for robust point set registration.

本文提出一种基于图论的方法来评估模拟LiDAR点云与真实扫描之间的结构保真度，通过比较图谱指标和几何基线，解决了传统几何度量无法捕捉结构差异的问题。

This work addresses the challenge of cross-modal feature generalization between images and point clouds, which suffer from significant modality gaps and lead to a notable drop in registration accuracy in unseen scenes. To overcome this, the paper proposes a novel registration method based on heterogeneous graph neural networks. It introduces, for the first time, a heterogeneous graph mechanism that jointly models correspondences between 2D image regions and 3D point cloud regions. Cross-modal features are adaptively aligned through multi-path feature interactions guided by heterogeneous edges, while reliable matches are refined via intra-graph node-edge consistency constraints. Evaluated across six indoor and outdoor cross-domain benchmarks, the proposed method consistently outperforms existing approaches in both registration accuracy and generalization capability.

FlashReg通过GPU加速和优化的图构建及三节点团搜索方法，解决了点云配准中计算和内存密集的问题，实现实时位姿估计。

本文提出PESTO算法，利用四面体作为通用特征解决LiDAR点云配准问题，特别是在重叠区域有限的环境下，并证明了其在最坏情况下的误差界限。