Multiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression

The non-uniform distribution and extremely sparse nature of the LiDAR point cloud (LPC) bring significant challenges to its high-efficient compression. This paper proposes a novel end-to-end, fully-factorized deep framework that encodes the original LPC into an octree structure and hierarchically decomposes the octree entropy model in layers. The proposed framework utilizes a hierarchical latent variable as side information to encapsulate the sibling and ancestor dependence, which provides sufficient context information for the modelling of point cloud distribution while enabling the parallel encoding and decoding of octree nodes in the same layer. Besides, we propose a residual coding framework for the compression of the latent variable, which explores the spatial correlation of each layer by progressive downsampling, and model the corresponding residual with a fully-factorized entropy model. Furthermore, we propose soft addition and subtraction for residual coding to improve network flexibility. The comprehensive experiment results on the LiDAR benchmark SemanticKITTI and MPEG-specified dataset Ford demonstrates that our proposed framework achieves state-of-the-art performance among all the previous LPC frameworks. Besides, our end-to-end, fully-factorized framework is proved by experiment to be high-parallelized and time-efficient and saves more than 99.8% of decoding time compared to previous state-of-the-art methods on LPC compression

D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction

The non-uniformly distributed nature of the 3D dynamic point cloud (DPC) brings significant challenges to its high-efficient inter-frame compression. This paper proposes a novel 3D sparse convolution-based Deep Dynamic Point Cloud Compression (D-DPCC) network to compensate and compress the DPC geometry with 3D motion estimation and motion compensation in the feature space. In the proposed D-DPCC network, we design a {\it Multi-scale Motion Fusion} (MMF) module to accurately estimate the 3D optical flow between the feature representations of adjacent point cloud frames. Specifically, we utilize a 3D sparse convolution-based encoder to obtain the latent representation for motion estimation in the feature space and introduce the proposed MMF module for fused 3D motion embedding. Besides, for motion compensation, we propose a 3D {\it Adaptively Weighted Interpolation} (3DAWI) algorithm with a penalty coefficient to adaptively decrease the impact of distant neighbors. We compress the motion embedding and the residual with a lossy autoencoder-based network. To our knowledge, this paper is the first work proposing an end-to-end deep dynamic point cloud compression framework. The experimental result shows that the proposed D-DPCC framework achieves an average 76\% BD-Rate (Bjontegaard Delta Rate) gains against state-of-the-art Video-based Point Cloud Compression (V-PCC) v13 in inter mode

The New PI3K/mTOR Inhibitor GNE-477 Inhibits the Malignant Behavior of Human Glioblastoma Cells

The most common primary central nervous system tumor in adults is glioblastoma multiforme (GBM). The high invasiveness of GBM cells is an important factor leading to inevitable tumor recurrence and a poor prognosis of patients. GNE-477, a novel PI3K/mTOR inhibitor, has been reported to exert antiproliferative effects on other cancer cells. However, researchers have not clearly determined whether GNE-477 produces antitumor effects on GBM. In the present study, GNE-477 significantly inhibited the proliferation, migration and invasion of U87 and U251 cells. In addition, GNE-477 also induced apoptosis of GBM cells, arresting the cell cycle in G0/G1 phase. More importantly, GNE-477 also reduced the levels of AKT and mTOR phosphorylation in the AKT/mTOR signaling pathway in a concentration-dependent manner. An increase in AKT activity induced by SC79 rescued the GNE-477-mediated inhibition of GBM cell proliferation and apoptosis. The antitumor effects of GNE-477 and the regulatory effects on related molecules were further confirme