Point cloud is a crucial 3D representation that plays significant role in fields such as VR/AR and digital museums. With the increasing amount of point cloud data, many compression and rendering methods are proposed, which often focus on either 3D or 2D visual quality. However, with the rapid advancements in display technology, there is a growing expectation for improved 2D and 3D visual quality while using lower bandwidth. To address these challenges, we present PCCRender, a novel end-to-end framework that achieves high-quality attribute preservation and rendering capabilities while maintaining low bandwidth requirements. To achieve efficient variable rate compression, we introduce Geometry-Invariant Rate Adjustment (GIRA) module that mitigates the influence of point cloud density during rate adjustment. Additionally, to improve decoding speed, we develop Uneven Four-Group Context Model (UFCM), achieving a trade-off between the accuracy and complexity of entropy parameter prediction. Moreover, we design Voxel to Gaussian Primitive Converter (V2G-Converter) which generates Gaussian primitives from decoded point clouds, enabling differentiable rendering. Our unified optimization framework jointly minimizes bit rate while maximizing both 2D rendered image quality and point cloud attribute fidelity. Experimental results demonstrate that PCCRender achieves state-of-the-art performance. Compared to the GPCC v23 (GS Render) method, our framework achieves 10.58% BD-Rate gain in attribute compression and 1.5 dB BD-PSNR increase in 2D rendered visual quality. These compelling results, combined with support for variable rate and free viewpoint rendering, establish PCCRender as a practical solution for the next generation point cloud applications.

Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity, limited compression modes, and a lack of support for variable rate, which restrict the practical application of these methods. In order to promote the development of practical point cloud compression, we propose an efficient unified point cloud geometry compression framework, dubbed as UniPCGC. It is a lightweight framework that supports lossy compression, lossless compression, variable rate and variable complexity. First, we introduce the Uneven 8-Stage Lossless Coder (UELC) in the lossless mode, which allocates more computational complexity to groups with higher coding difficulty, and merges groups with lower coding difficulty. Second, Variable Rate and Complexity Module (VRCM) is achieved in the lossy mode through joint adoption of a rate modulation module and dynamic sparse convolution. Finally, through the dynamic combination of UELC and VRCM, we achieve lossy compression, lossless compression, variable rate and complexity within a unified framework. Compared to the previous state-of-the-art method, our method achieves a compression ratio (CR) gain of 8.1% on lossless compression, and a Bjontegaard Delta Rate (BD-Rate) gain of 14.02% on lossy compression, while also supporting variable rate and variable complexity.

The increasing demand for large-scale, high-quality datasets in dynamic point cloud compression (PCC) and human visual perception research underscores the limitations of existing datasets, which are often constrained by limited scale and insufficient dynamism, hindering algorithm validation and perceptual analysis in complex scenarios. To address this gap, we present DPCSet, a comprehensive dynamic point cloud dataset designed to support advanced research in PCC, human perception, and related domains. Comprising 100 dynamic object point clouds-the largest collection of its kind-DPCSet includes 200-frame sequences with geometry and attribute information, capturing diverse object types across real and virtual environments. Organized into seven superclasses, the dataset ensures broad scenario coverage. By rigorous selection, format conversion, quantization, DPCSet delivers standardized, high-precision point cloud data. Evaluation of multiple compression algorithms on a curated subset demonstrates DPCSet's efficacy in assessing trade-offs between compression efficiency and quality loss, positioning it as a potential benchmark for PCC. Furthermore, just noticeable distortion (JND) experiments on a compression-distorted subset reveal distinct perceptual characteristics of dynamic point clouds, offering valuable insights for perception-driven compression algorithms. The dataset is released at https://openi.pcl.ac.cn/gaowx/DPCSet.

Summary: Gene expression changes over the lifespan and varies among different tissues or cell types. Gene co-expression also changes by sex, age, different tissues or cell types. However, gene expression under the normal state and gene co-expression in the human brain has not been fully defined and quantified. Here we present a database named Brain EXPression Database (BrainEXP) which provides spatiotemporal expression of individual genes and co-expression in normal human brains. BrainEXP consists of 4567 samples from 2863 healthy individuals gathered from existing public databases and our own data, in either microarray or RNA-Seq library types. We mainly provide two analysis results based on the large dataset: (i) basic gene expression across specific brain regions, age ranges and sexes; (ii) co-expression analysis from different platforms. Availability and implementation: http://www.brainexp.org/. Supplementary information: Supplementary data are available at Bioinformatics online.

Recent developments in robotics sensing using either the active sensor-laser range finder (LRF) or passive sensors-camera systems have shown that the existing approaches are able to estimate the motion and reconstruct the environment in a typical GPS-denied environment such as indoor environments. However, for a 2D LRF, it can only provide a planar measurement due to the hardware limitations and the cost for 3D LRF is still too high for robotic systems. For camera systems, the 3D perception capability and lightweight are promising while it is not effective in long range, low illumination and low textured environment com- pared to the LRF. In the proposed framework, our first contribution is a senor integration system that combines a stereo camera with a rotating LRF. The stereo camera can achieve a fast and smooth motion estimation and the rotating LRF could construct a dense 3D environment. Our second contribution is an integration of a fast feature-based motion estimation with an accurate shape matching refinement. The proposed approaches have been built in our customized unmanned aerial vehicle (UAV) platform. We have verified our proposed approach through a series of extensive evaluations in clustered indoor environments and open outdoor environments.