GRIP is a pose‑conditioned refinement framework that improves pixel‑to‑point matching for image‑to‑point‑cloud registration. It mitigates the mismatch between grid‑based image descriptors and unordered point cloud descriptors by softly rendering learned 3D point features onto the image grid using Gaussian feature splatting. The resulting rendered point‑derived feature map is fused with image features via a pixel‑aligned transformer, enabling visual semantic and geometric cues to interact in a shared 2D representation, which is then decoded and propagated to finer resolutions for dense correspondence estimation and final pose refinement. Experiments on RGB‑D Scenes V2 and 7 Scenes show state‑of‑the‑art inlier ratios and competitive registration recall, especially under stricter evaluation thresholds.
By Karim Slimani, Catherine Achard, Eric Marchand, Brahim Tamadazte
The paper introduces DPA-I2P, a depth‑guided projective alignment method for image‑to‑point‑cloud registration in autonomous driving. It employs Ray‑Conditioned Metric Depth Encoding and Projection‑Consistent Vision Lifting to align depth and visual cues geometrically, and uses Cross‑Modal Query Pruning to filter unreliable matches during refinement. Experiments on KITTI and nuScenes show significant improvements, reducing rotation and translation errors by up to 55.6% compared to existing implicit baselines.
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.
By Tianyu Li, Yanghong Lin, Shudong Zhou, Kui Yang, Jingru Zhang, Li Fang, Wei Yao
The paper introduces DPA-I2P, a depth-guided projective alignment method for image-to-point-cloud registration in autonomous driving. It employs Ray-Conditioned Metric Depth Encoding and Projection-Consistent Vision Lifting to align depth and visual cues geometrically, and uses Cross-Modal Query Pruning to enhance matching stability. Experiments on KITTI and nuScenes show significant reductions in rotation and translation errors compared to existing implicit baselines.
By Wenxin Zhang, Hang Li, Zhiwei Xu, Qiankun Dong, Gang Wang, Tao Li
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.
By Ray Zhang, Marcus Greiff, Thomas Lew, John Subosits
Category-level object pose estimation seeks to recover a similarity transform $(R,t,s)$ for unseen instances without instance-specific CAD models. Most competitive methods are correspondence-based: pr...