Representation learning for two partially overlapping point clouds remains an open challenge in unsupervised point cloud registration (U-PCR). In this article, we introduce RegiFormer, a geometric local-to-global transformer (GLGT)-based unsupervised framework equipped with a self-augmentation (SA) strategy, for point cloud registration. The GLGT not only aggregates features from local neighborhoods but also extracts global intrarelationships within the entire point cloud using a transformation-invariant geometry embedding. In addition, it enhances the interrelationships between paired point clouds. To overcome the limited ability of U-PCR methods to learn alignment knowledge, we design an SA strategy that can be flexibly integrated into advanced models, significantly boosting their registration performance. Extensive experiments, conducted on five popular synthetic and real-scanned benchmarks, demonstrate the superior performance of RegiFormer compared to state-of-the-art methods, both qualitatively and quantitatively.

Exploiting long-range semantic contexts and geometric information is crucial to infer salient objects from RGB and depth features. However, existing methods mainly focus on excavating local features within fixed regions by continuously feeding forward networks. In this article, we introduce Dynamic Message Propagation (DMP) to dynamically learn context information within more flexible regions. We integrate DMP into a Siamese-based network to process the RGB image and depth map separately and design a multi-level feature fusion module to explore cross-level information between refined RGB and depth features. Extensive experiments show clear improvements of our method over 17 methods on six benchmark datasets for RGB-D salient object detection (SOD). Additionally, our method outperforms its competitors for the video SOD task. Code is available at https://github.com/chenbaian-cs/DMPNet .

Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep learning. The traditional wisdom of convolution characterises feature correspondences indistinguishably among 3D points, arising an intrinsic limitation of poor distinctive feature learning. In this article, we propose Adaptive Graph Convolution (AGConv) for wide applications of point cloud analysis. AGConv generates adaptive kernels for points according to their dynamically learned features. Compared with the solution of using fixed/isotropic kernels, AGConv improves the flexibility of point cloud convolutions, effectively and precisely capturing the diverse relations between points from different semantic parts. Unlike the popular attentional weight schemes, AGConv implements the adaptiveness inside the convolution operation instead of simply assigning different weights to the neighboring points. Extensive evaluations clearly show that our method outperforms state-of-the-arts of point cloud classification and segmentation on various benchmark datasets. Meanwhile, AGConv can flexibly serve more point cloud analysis approaches to boost their performance. To validate its flexibility and effectiveness, we explore AGConv-based paradigms of completion, denoising, upsampling, registration and circle extraction, which are comparable or even superior to their competitors.

Self-supervised learning is attracting large attention in point cloud understanding. However, exploring discriminative and transferable features still remains challenging due to their nature of irregularity. We propose a geometrically and adaptively masked auto-encoder on point clouds for self-supervised learning, termedPointGame. PointGame contains two core components: GATE and EAT. GATE stands for the geometrical and adaptive token embedding module; it not only absorbs the conventional wisdom of geometric descriptors that captures the surface shape effectively, but also exploits adaptive saliency to focus on the salient part of a point cloud. EAT stands for the external attention-based Transformer encoder with linear computational complexity, which increases the efficiency of the whole pipeline. Unlike cutting-edge unsupervised learning models, PointGame leverages geometric descriptors to perceive surface shapes and adaptively mines discriminative features from training data. PointGame showcases clear advantages over its competitors on various downstream tasks under both global and local fine-tuning strategies. The code and pre-trained models will be publicly available.

Abstract High‐confidence overlap prediction and accurate correspondences are critical for cutting‐edge models to align paired point clouds in a partial‐to‐partial manner. However, there inherently exists uncertainty between the overlapping and non‐overlapping regions, which has always been neglected and significantly affects the registration performance. Beyond the current wisdom, we propose a novel uncertainty‐aware overlap prediction network, dubbed UTOPIC, to tackle the ambiguous overlap prediction problem; to our knowledge, this is the first to explicitly introduce overlap uncertainty to point cloud registration. Moreover, we induce the feature extractor to implicitly perceive the shape knowledge through a completion decoder, and present a geometric relation embedding for Transformer to obtain transformation‐invariant geometry‐aware feature representations. With the merits of more reliable overlap scores and more precise dense correspondences, UTOPIC can achieve stable and accurate registration results, even for the inputs with limited overlapping areas. Extensive quantitative and qualitative experiments on synthetic and real benchmarks demonstrate the superiority of our approach over state‐of‐the‐art methods.