Masked point modeling methods have recently achieved great success in self-supervised learning for point cloud data. However, these methods are sensitive to rotations and often exhibit sharp performance drops when encountering rotational variations. In this paper, we propose a novel Rotation-Invariant Masked AutoEncoders (RI-MAE) to address two major challenges: 1) achieving rotation-invariant latent representations, and 2) facilitating self-supervised reconstruction in a rotation-invariant manner. For the first challenge, we introduce RI-Transformer, which features disentangled geometry content, rotation-invariant relative orientation and position embedding mechanisms for constructing rotation-invariant point cloud latent space. For the second challenge, a novel dual-branch student-teacher architecture is devised. It enables the self-supervised learning via the reconstruction of masked patches within the learned rotation-invariant latent space. Each branch is based on an RI-Transformer, and they are connected with an additional RI-Transformer predictor. The teacher encodes all point patches, while the student solely encodes unmasked ones. Finally, the predictor predicts the latent features of the masked patches using the output latent embeddings from the student, supervised by the outputs from the teacher. Extensive experiments demonstrate that our method is robust to rotations, achieving the state-of-the-art performance on various downstream tasks.

Point cloud completion aims to reconstruct complete 3D shapes from partial 3D point clouds. With advancements in deep learning techniques, various methods for point cloud completion have been developed. Despite achieving encouraging results, a significant issue remains: these methods often overlook the variability in point clouds sampled from a single 3D object surface. This variability can lead to ambiguity and hinder the achievement of more precise completion results. Therefore, in this study, we introduce a novel point cloud completion network, namely Dual-Codebook Point Completion Network (DC-PCN), following an encder-decoder pipeline. The primary objective of DC-PCN is to formulate a singular representation of sampled point clouds originating from the same 3D surface. DC-PCN introduces a dual-codebook design to quantize point-cloud representations from a multilevel perspective. It consists of an encoder-codebook and a decoder-codebook, designed to capture distinct point cloud patterns at shallow and deep levels. Additionally, to enhance the information flow between these two codebooks, we devise an information exchange mechanism. This approach ensures that crucial features and patterns from both shallow and deep levels are effectively utilized for completion. Extensive experiments on the PCN, ShapeNet_Part, and ShapeNet34 datasets demonstrate the state-of-the-art performance of our method.

The millimeter-wave (mmWave) radar-based gait recognition technology has attracted significant attention due to its cost-effectiveness and weather resilience. The majority of existing methods are primarily designed for the recognition of gait patterns on the fixed route, and they achieve significant performance. However, fewer advancements have been made in gait pattern recognition for the free route, primarily owing to the challenges posed by data sparsity and route diversity in such contexts. To take those issues into account, we present a novel framework named Gait Spatio-Temporal Network (GSTNet), designed for efficient human gait recognition from spatiotemporal features. The GSTNet is composed of Multi-Temporal Resolution DGCNN (MTRD) and Dynamic Feature Capturing Module (DFCM). The MTDR regroups frames before edge convolution, extracting not only the spatial features of each frame but also the dynamic features from neighboring frames to cope with relative data sparsity. The DFCM adaptively generates weights for each frame through temporal features to handle diverse gait samples. Experiments demonstrate our GSTNet achieves state-of-the-art on the mmGait and STPointGCN datasets. Additionally, we provide results from the ablation study to further validate the efficacy of the proposed framework.