Efficient Point Cloud Geometry Compression (PCGC) with a lower bits per point (BPP) and higher peak signal-to- noise ratio (PSNR) is essential for the transportation of large-scale 3D data. Although octree-based entropy models can reduce BPP without introducing geometry distortion, existing CNN-based models struggle with limited receptive fields to capture long-range dependencies, while Transformer-built architectures always neglect fine-grained details due to their reliance on global self-attention. This paper presents a Transformer-efficient occupancy prediction Network, termed TopNet, to overcome these challenges by developing several novel components designed to enhance both global context modeling and local structure preservation: Locally-enhanced Context Encoding (LeCE) for improving local structural awareness and enhancing the translation-invariance of the octree nodes, Adaptive-Length Sliding Window Attention (AL-SWA) for capturing both global and local dependencies while adaptively adjusting attention weights based on the input window length, Spatial-Gated-enhanced Channel Mixer (SG-CM) for efficient feature aggregation from ancestors and siblings, and Latent-guided Node Occupancy Predictor (LNOP) for improving prediction accuracy of spatially adjacent octree nodes in local context. Comprehensive experiments across three large-scale outdoor sparse LiDAR datasets, including SemanticKITTI, nuScenes, and LiDAR-CS, as well as two indoor dense human body datasets, including 8iVFB and MVUB, and one indoor dense scenario dataset, ScanNet, demonstrate that our TopNet achieves state-of-the-art compression performance with fewer parameters.

The insufficient exploitation of spatial correlations among octree node context and feature interactions across spatial and channel dimensions limits the reconstruction performance of current point cloud geometry compression (PCGC) models. To solve these issues, this paper presents an octree-structured context entropy model OctGLP-Net with global-local perception, which mainly consists of a Local Spatial Perception (LocSP) module, a Scaled-cosine Attention based Spatial Interaction (SASI) module, and a Locally-enhanced Feed-forward Spatial and Channel Interaction (LFSCI) module. First, we propose the LocSP to extract local context information from octree nodes. Then, we introduce the SASI to fully exploit the spatial correlations among nodes. Finally, to effectively interact local features across spatial and channel dimensions, we devise a LFSCI network by employing depth-wise and point-wise convolutions to realize fine-grained local feature extraction from octree nodes. Experimental results on both sparse LiDAR and dense object benchmark datasets demonstrate that our method achieves state-of-the-art lossy/lossless compression performance. Our method obtains higher reconstruction quality (D1/D2 PSNR) and smaller chamfer distance (CD) at similar bits per point (BPP) on the SemanticKITTI, nuScenes, and LiDAR-CS datasets, and lower bitrate on the Owlii, 8iVFB and MVUB datasets. Significantly, the proposed OctGLP-Net also exhibits strong generalization abilities when applied to unseen nuScenes and LiDAR-CS datasets. In addition, downstream object detection task on the nuScenes dataset with different compression precisions further demonstrate the superiority and robustness of our method.

Efficient Point Cloud Geometry Compression (PCGC) with a lower bits per point (BPP) and higher peak signalto-noise ratio (PSNR) is essential for the transportation of large-scale 3D data. Although octree-based entropy models can reduce BPP without introducing geometry distortion, existing CNN-based models struggle with limited receptive fields to capture long-range dependencies, while Transformer-built architectures always neglect fine-grained details due to their reliance on global selfattention. In this paper, we propose a Transformer-efficient occupancy prediction Network, termed TopNet, to overcome these challenges by developing several novel components: Locally-enhanced Context Encoding (LeCE) for enhancing the translation-invariance of the octree nodes, Adaptive-Length Sliding Window Attention (ALSWA) for capturing both global and local dependencies while adaptively adjusting attention weights based on the input window length, Spatial-Gated-enhanced Channel Mixer (SG-CM) for efficient feature aggregation from ancestors and siblings, and Latent-guided Node Occupancy Predictor (LNOP) for improving prediction accuracy of spatially adjacent octree nodes. Comprehensive experiments across both indoor and outdoor point cloud datasets demonstrate that our TopNet achieves state-ofthe-art performance with fewer parameters, further advancing the reduction-efficiency boundaries of PCGC. The code is available at https://github.com/xinjiewang1995/TopNet.

Efficiently compressing large-scale point cloud data under limited bandwidth and computing resource conditions has become a critical issue to be addressed in mobile computing platforms. Although the octree structure can efficiently represent large-scale and complex point clouds, existing octree-based Point Cloud Geometry Compression (PCGC) approaches typically focus on exploiting either spatial or channel features individually, neglecting the interaction across spatial-channel dimensions. In addition, current approaches are also limited to small-scale point clouds due to reliance on global Transformer or local convolutional neural network (CNN). To solve these issues, we introduce GCFI-Net, a global-local cross-spatial-channel feature interaction network for predicting the occupancy probability distribution of each octree node in this paper. In the GCFI-Net, we propose a Multiscale Convolutional Fusion-based Spatial Interaction (MCFSI) module to capture global context and model spatial interactions, and a Global-Local Cross-Channel Interaction (GLCCI) module with dual pathways to integrate global and local cross-channel information. Additionally, we propose a Multiscale-enhanced Spatial and Channel Interaction (MSCI) module to aggregate features from ancestor and sibling nodes, which further enhances the octree node representation ability. Extensive experiments on large-scale sparse LiDAR and dense human body point clouds demonstrate that the proposed GCFI-Net achieves superior compression performance with fewer parameters compared to state-of-the-art PCGC methods.

To further promote the development of multimodal point cloud completion, we contribute a large-scale multimodal point cloud completion benchmark ModelNet-MPC with richer shape categories and more diverse test data, which contains nearly 400,000 pairs of high-quality point clouds and rendered images of 40 categories. Besides the fully supervised point cloud completion task, two additional tasks including denoising completion and zero-shot learning completion are proposed in ModelNet-MPC, to simulate real-world scenarios and verify the robustness to noise and the transfer ability across categories of current methods. Meanwhile, considering that existing multimodal completion pipelines usually adopt a unidirectional fusion mechanism and ignore the shape prior contained in the image modality, we propose a Dual-Modality Feature Interaction Network (DuInNet) in this paper. DuInNet iteratively interacts features between point clouds and images to learn both geometric and texture characteristics of shapes with the dual feature interactor. To adapt to specific tasks such as fully supervised, denoising, and zero-shot learning point cloud completions, an adaptive point generator is proposed to generate complete point clouds in blocks with different weights for these two modalities. Extensive experiments on the ShapeNet-ViPC and ModelNet-MPC benchmarks demonstrate that DuInNet exhibits superiority, robustness and transfer ability in all completion tasks over state-of-the-art methods. The code and dataset will be available athttps://github.com/xinpuliu/DuInNet.

Abstract Point cloud completion aims to reconstruct detailed structures from partial observations. However, previous methods often suffered from inaccurate neighbourhood feature extraction and rough reconstruction of complex structures, which is difficult to complete detailed semantic shapes. To solve this problem, we present a method that applies dynamic transformers with adaptive neighbourhood feature fusion operations to resume complete point clouds. Firstly, we propose an adaptive neighbourhood feature extraction module, which contains a learnable global neighbourhood selection strategy and a traditional local k‐nearest neighbour strategy to dynamically select neighbourhood points according to the shape of different objects. Secondly, we observed that traditional point generation methods based on folding‐series operations limit their capacity of generating complex and faithful shapes. Inspired by cell division, we regard the process of reconstructing as point splitting and propose a genetic hierarchical point generation module, which means current points can inherit shape features from previous points and generate more detailed structures of their own. Extensive experiments of point cloud completion are carried out on PCN and Completion3D datasets to verify the effectiveness of our method. The average category chamfer distances of our method are 7.17(×10 −3 ) in PCN and 7.96(×10 −4 ) in Completion3D, which has better completion performance than other methods.

Completing shapes of point clouds from partial scans is a fundamental problem for 3-D vision and remote sensing. However, recent methods mainly relied on K-nearest neighbors (KNN) operations to extract local features of point clouds, which are susceptible to outliers and have limited ability to capture features from long-range context information. In this letter, we propose a new framework with an encoder–decoder architecture, named adaptive global feature augmentation network (AGFA-Net) for point cloud completion. The network mainly consists of spatial and channel attention blocks. Spatial attention blocks are used to replace KNN operations and aggregate global features adaptively by calculating per-point attention values, and channel attention blocks are used to augment useful features of geometric details. Meanwhile, several skip connections are added between different attention blocks to selectively convey geometric features from local regions of partial point clouds to the completion process. Experimental results and analyses demonstrate that our method can generate finer shapes of point clouds and outperforms other state-of-the-art methods under widely used benchmark point completion network (PCN) dataset and several terrestrial laser scanning (TLS) data.