Heuristic information for consensus set sampling is essential for correspondence-based point cloud registration, but existing approaches typically rely on supervised learning or expert-driven parameter tuning. In this work, we propose HeMoRa, a new unsupervised framework that trains a Heuristic information Generator (HeGen) to estimate sampling probabilities for correspondences using a Multi-order Reward Aggregator (MoRa) loss. The core of MoRa is to train HeGen through extensive trials and feedback, enabling unsupervised learning. While this process can be implemented using policy optimization, directly applying the policy gradient to optimize HeGen presents challenges such as sensitivity to noise and low reward efficiency. To address these issues, we propose a Maximal Reward Propagation (MRP) mechanism that enhances the training process by prioritizing noise-free signals and improving reward utilization. Experimental results show that equipped with HeMoRa, the consensus set sampler achieves improvements in both robustness and accuracy. For example, on the 3DMatch dataset with FCGF feature, the registration recall of our unsupervised methods (Ours+SM and Ours+SC2) even outperforms the state-of-the-art supervised method VBreg. Our code is available at HeMoRa.

Robust estimation is essential in correspondence-based Point Cloud Registration (PCR). Existing methods using maximal clique search in compatibility graphs achieve high recall but suffer from exponential time complexity, limiting their use in time-sensitive applications. To address this challenge, we propose a fast and robust estimator, TurboReg, built upon a novel lightweight clique, TurboClique, and a highly parallelizable Pivot-Guided Search (PGS) algorithm. First, we define the TurboClique as a 3-clique within a highly-constrained compatibility graph. The lightweight nature of the 3-clique allows for efficient parallel searching, and the highly-constrained compatibility graph ensures robust spatial consistency for stable transformation estimation. Next, PGS selects matching pairs with high SC$^2$ scores as pivots, effectively guiding the search toward TurboCliques with higher inlier ratios. Moreover, the PGS algorithm has linear time complexity and is significantly more efficient than the maximal clique search with exponential time complexity. Extensive experiments show that TurboReg achieves state-of-the-art performance across multiple real-world datasets, with substantial speed improvements. For example, on the 3DMatch+FCGF dataset, TurboReg (1K) operates $208.22\times$ faster than 3DMAC while also achieving higher recall. Our code is accessible at \href{https://github.com/Laka-3DV/TurboReg}{\texttt{TurboReg}}.

Existing Transformer-based dehazing methods for remote sensing (RS) images, to avoid quadratic computation complexity with respect to the feature map size, either perform self-attention mechanisms within local windows or capture long-range dependencies in the channel dimension rather than spatial. Each of these methods has its drawbacks. To address these limitations, we propose the Proxy and Cross-Stripes Integration Transformer (PCSformer) for RS image dehazing. PCSformer introduces two innovative Transformer blocks, i.e., sliding cross-stripes Transformer block and local proxy-based global Transformer block. The former allows us to directly model long-range dependencies and capture rich contextual information for large-scale objects in RS images. The latter seeks valuable information for thick haze regions within the whole feature map, generating more consistent and realistic scene details for such regions. Both achieve a large receptive field with cost-effective computational complexity within a single Transformer block. Furthermore, we introduce a shallow deep model with a small receptive field to conduct local refinement, which can mitigate artifacts associated with a large receptive field. Finally, to facilitate the better application of dehazing models to downstream visual tasks, we contribute two large-scale datasets for RS image dehazing. Experiments indicate that the dehazing models trained on our datasets can better assist downstream visual tasks under hazy atmospheric conditions compared to the dehazing models trained on existing datasets. Quantitative and qualitative experiments demonstrate that the proposed PCSformer significantly outperforms existing state-of-the-art techniques on dehazing benchmarks, particularly excelling in the restoration of thick haze scenes. The code and datasets are available athttps://github.com/SmileShaun/PCSformer.