# RKHS-BA: A Robust Correspondence-Free Multi-View Registration Framework with Semantic Point Clouds

arXiv: https://arxiv.org/abs/2403.01254 · HTML: https://mlsanity.com/paper/2403.01254/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2024.

## Built on by (most cited first, 3 of 3)

- [DynaWeightPnP: Toward global real-time 3D-2D solver in PnP without correspondences](https://mlsanity.com/paper/2409.18457/) (2024)
- [Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization](https://mlsanity.com/paper/2606.10019/) (2026)
- [Spectral Consistency-Guided Multiview Point Cloud Registration for Low-Overlap Scenes](https://mlsanity.com/paper/2609.12417/) (2026)

## Abstract (arXiv)

This work reports a novel multi-frame Bundle Adjustment (BA) framework called RKHS-BA. It uses continuous landmark representations that encode RGB-D/LiDAR and semantic observations in a Reproducing Kernel Hilbert Space (RKHS). With a correspondence-free pose graph formulation, the proposed system constructs a loss function that achieves more generalized convergence than classical point-wise convergence. We demonstrate its applications in multi-view point cloud registration, sliding-window odometry, and global LiDAR mapping on simulated and real data. It shows highly robust pose estimations in extremely noisy scenes and exhibits strong generalization with various types of semantic inputs. The open source implementation is released in https://github.com/UMich-CURLY/RKHS_BA.