The paper investigates the theoretical relationship between maximum clique and maximum consensus formulations used in robust point cloud registration. It demonstrates that a maximum clique can incur arbitrarily large alignment error and be much larger than the maximum consensus set at any fixed tolerance, while also deriving geometric conditions that bound this error. The authors provide sharp error bounds for the translation subproblem with fixed rotation, analyze group‑k consistency, and identify the minimal group sizes needed to guarantee alignment error bounds independent of scale and geometry.
Maximum clique and maximum consensus are widely used formulations for robust point cloud registration in robotics. Maximum consensus seeks one transformation that aligns as many matches as possible, w...
The paper introduces GMPCR, a non‑learning spectral consistency‑guided framework for multiview point cloud registration in low‑overlap scenes. GMPCR refines initial correspondences into a second‑order compatibility structure, uses spectral analysis to filter unreliable matches and select informative scan pairs, and then applies maximal‑clique hypothesis generation for robust relative transformations. The resulting sparse pose graph is further refined with an adaptive history‑aware synchronization scheme, and a recovery mechanism allows previously down‑weighted edges to regain confidence, achieving high registration recalls on benchmark datasets while reducing computational cost.
arXiv:2609.25375v1 Announce Type: cross Abstract: Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated...
arXiv:2610.10408v1 Announce Type: cross Abstract: Procrustes-Wasserstein alignment jointly estimates a matching and rotation without supplied correspondences, but alternating minimization can stop at...
arXiv:2609.37071v1 Announce Type: new Abstract: Non-rigid point-cloud registration aims to find the corresponding target point for each point on a deforming source surface. Point-level matching keeps...
arXiv:2610.34271v1 Announce Type: new Abstract: We propose a novel method for robust point cloud registration with only one tunable parameter. Our method, dubbed TLP, uses truncated $L_p$ functions w...
The paper presents a lightweight 6D pose tracking method that operates on relative pose constraints instead of point-based optimization, using uncertainty derived from geometric alignment to weight these constraints. By avoiding direct inclusion of noisy point measurements, the approach achieves fast and robust long-term tracking with computational costs far lower than reconstruction-based trackers. Experiments on four real-world benchmarks show comparable accuracy to state-of-the-art methods while maintaining real-time performance.
arXiv:2608. 09706v1 Announce Type: cross Abstract: Large language models can write parametric CAD programs from a natural-language description (text-to-CAD generation), but a single sample is often wrong.
FlashReg is a GPU‑accelerated method for point cloud registration that bypasses the construction of dense second‑order compatibility graphs. It builds a sparse second‑order graph directly from a binary first‑order graph using Fast First‑ and Second‑Order Graph (FFSOG) construction, then performs a dataflow‑optimized 3‑clique search to select correspondence pivots and enumerate triples. On indoor and outdoor benchmarks, FlashReg achieves 2–3× lower correspondence‑to‑pose latency than TurboReg while using roughly half the peak tensor memory on an embedded GPU, making it suitable for high‑throughput onboard perception pipelines.
arXiv:2606. 10019v1 Announce Type: cross Abstract: We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings.
arXiv:2508.13488v2 Announce Type: replace-cross Abstract: Loop closure detection is important for simultaneous localization and mapping (SLAM), which associates current observations with historical k...
arXiv:2606. 27818v1 Announce Type: cross Abstract: We present MMD-Reg, a novel correspondence-free approach to point-cloud registration that is differentiable and has linear computational complexity in the number of points.