Self-supervised pre-training is essential for 3D point cloud representation learning, as annotating their irregular, topology-free structures is costly and labor-intensive. Masked autoencoders (MAEs) offer a promising framework but rely on explicit positional embeddings, such as patch center coordinates, which leak geometric information and limit data-driven structural learning. In this work, we propose Point-MaDi, a novel Point cloud Masked autoencoding Diffusion framework for pre-training that integrates a dual-diffusion pretext task into an MAE architecture to address this issue. Specifically, we introduce a center diffusion mechanism in the encoder, noising and predicting the coordinates of both visible and masked patch centers without ground-truth positional embeddings. These predicted centers are processed using a transformer with self-attention and cross-attention to capture intra- and inter-patch relationships. In the decoder, we design a conditional patch diffusion process, guided by the encoder's latent features and predicted centers to reconstruct masked patches directly from noise. This dual-diffusion design drives comprehensive global semantic and local geometric representations during pre-training, eliminating external geometric priors. Extensive experiments on ScanObjectNN, ModelNet40, ShapeNetPart, S3DIS, and ScanNet demonstrate that Point-MaDi achieves superior performance across downstream tasks, surpassing Point-MAE by 5.51\% on OBJ-BG, 5.17\% on OBJ-ONLY, and 4.34\% on PB-T50-RS for 3D object classification on the ScanObjectNN dataset.

Keypoint detection and descriptor matching are two vital steps in the 3D feature extraction framework, but they are difficult to learn in an end-to-end fashion due to their inherent discreteness. To tackle the non-differentiable operations, we formulate feature extraction as a decision-making problem: the network is treated as a policy pool that can make probabilistic estimations for keypoint selection and feature matching, supervised by maximizing a reward expectation of actions. In this way, we propose a novel end-to-end training paradigm of 3D feature extraction based on the stochastic policy gradient method, named Reinforced Detectors and Descriptors (RDD). Firstly, we propose a local-to-global probabilistic keypoint selection module that formulates the sampling probabilities of keypoints in a local-and-global mechanism to yield sparse and accurate keypoints. Secondly, we regard feature matching as an optimal transport problem and an efficient Sinkhorn method is leveraged to solve the optimal matching probabilities. In particular, we carefully design a reward function and derive gradients of probabilistic actions, thus overcoming the discreteness and providing reinforced supervision signals. Since our reward function is calculated from sampled keypoints rather than from randomly sampled points as in existing methods, the gap between training and inference is bridged. Experimental results demonstrate that our approach exceeds the quality of state-of-the-art methods and shows strong generalization ability. Remarkably, our approach can achieve significantly higher Registration Recall than other advanced methods when aligning scenes with a small number of keypoints, due to our highly accurate and repeatable detector.

Fragment assembly involves restoring broken objects to their original geometries, and has many applications, such as archaeological restoration. Existing learning based frameworks have shown potential for solving part assembly problems with semantic decomposition, but cannot handle such geometrical decomposition problems. In this work, we propose a novel assembly framework, proxy level hybrid Transformer, with the core idea of using a hybrid graph to model and reason complex structural relationships between patches of fragments, dubbed as proxies. To this end, we propose a hybrid attention module, composed of intra and inter attention layers, enabling capturing of crucial contextual information within fragments and relative structural knowledge across fragments. Furthermore, we propose an adjacency aware hierarchical pose estimator, exploiting a decompose and integrate strategy. It progressively predicts adjacent probability and relative poses between fragments, and then implicitly infers their absolute poses by dynamic information integration. Extensive experimental results demonstrate that our method effectively reduces assembly errors while maintaining fast inference speed. The code is available at https://github.com/521piglet/PHFormer.

Patch-to-point matching has become a robust way of point cloud registration. However, previous patch-matching methods employ superpoints with poor localization precision as nodes, which may lead to ambiguous patch partitions. In this paper, we propose a HybridPoint-based network to find more robust and accurate correspondences. Firstly, we propose to use salient points with prominent local features as nodes to increase patch repeatability, and introduce some uniformly distributed points to complete the point cloud, thus constituting hybrid points. Hybrid points not only have better localization precision but also give a complete picture of the whole point cloud. Furthermore, based on the characteristic of hybrid points, we propose a dual-classes patch matching module, which leverages the matching results of salient points and filters the matching noise of non-salient points. Experiments show that our model achieves state-of-the-art performance on 3DMatch, 3DLoMatch, and KITTI odometry, especially with 93.0% Registration Recall on the 3DMatch dataset. Our code and models are available at https://github.com/liyih/HybridPoint.

After decades of investigation, point cloud registration is still a challenging task in practice, especially when the correspondences are contaminated by a large number of outliers. It may result in a rapidly decreasing probability of generating a hypothesis close to the true transformation, leading to the failure of point cloud registration. To tackle this problem, we propose a transformation estimation method, named Hunter, for robust point cloud registration with severe outliers. The core of Hunter is to design a global-to-local exploration scheme to robustly find the correct correspondences. The global exploration aims to exploit guided sampling to generate promising initial alignments. To this end, a hypergraph-based consistency reasoning module is introduced to learn the high-order consistency among correct correspondences, which is able to yield a more distinct inlier cluster that facilitates the generation of all-inlier hypotheses. Moreover, we propose a preference-based local exploration module that exploits the preference information of top- k promising hypotheses to find a better transformation. This module can efficiently obtain multiple reliable transformation hypotheses by using a multi-initialization searching strategy. Finally, we present a distance-angle based hypothesis selection criterion to choose the most reliable transformation, which can avoid selecting symmetrically aligned false transformations. Experimental results on simulated, indoor, and outdoor datasets, demonstrate that Hunter can achieve significant superiority over the state-of-the-art methods, including both learning-based and traditional methods (as shown in Fig. 1). Moreover, experimental results also indicate that Hunter can achieve more stable performance compared with all other methods with severe outliers.

We propose a robust algorithm for aligning rigid, noisy, and partially overlapping red green blue-depth (RGB-D) point clouds. To address the problems of data degradation and uneven distribution, we offer three strategies to increase the robustness of the iterative closest point (ICP) algorithm. First, we introduce a salient object detection (SOD) method to extract a set of points with significant structural variation in the foreground, which can avoid the unbalanced proportion of foreground and background point sets leading to the local registration. Second, registration algorithms that rely only on structural information for alignment cannot establish the correct correspondences when faced with the point set with no significant change in structure. Therefore, a bidirectional color distance (BCD) is designed to build precise correspondence with bidirectional search and color guidance. Third, the maximum correntropy criterion (MCC) and trimmed strategy are introduced into our algorithm to handle with noise and outliers. We experimentally validate that our algorithm is more robust than previous algorithms on simulated and real-world scene data in most scenarios and achieve a satisfying 3-D reconstruction of indoor scenes.

In this paper, we propose an accurate global registration (TAG-Reg) algorithm for poor initialization and partially overlapping point clouds registration problem. Firstly, methods based on geometric structure information of points can get the accurate results, which is vulnerable to poor initialization. Meanwhile, existing features based global methods can solve poor initialization problem at a certain extent, but it cannot obtain accurate results. So, we combine the geometric structure information with feature as hybrid feature to solve poor initialization problem completely and obtain accurate results. Secondly, we introduce dynamic trimmed strategy combining with hybrid feature to deal with partially overlapping problem. Then, to improve the accuracy of our method, we utilize the probabilistic method to suppress noise. At last, we establish the TAG-Reg model and propose an iterative algorithm to solve this problem. Experimental results show that our TAG-Reg achieves state-of-the-art performance compared to existing non-deep learning and recent deep learning methods. Our source code will open at https://github.com/BiaoBiaoLi/TAG-Reg.

In this paper, we propose a deep weight global registration (DWG-Reg) algorithm for poor initialization and partially overlapping point clouds registration problem. Our DWG-Reg is based on three modules: a bidirectional nearest search strategy for correspondence, a convolutional network for correspondence confidence prediction which consists of Hybird Distance Generator, optimal annealing Parameter Prediction network and a robust kernel function, a weighted optimizer algorithm for closed-form pose estimation. Experimental results show that our DWG-Reg achieves state-of-the-art performance compared to existing non-deep learning and recent deep learning methods. Our source code will open at https://github.com/BiaoBiaoLi/DWG-Reg.