Non-Uniform Voxelisation for Point Cloud Compression
SensorsPoint cloud compression is essential for the efficient storage and transmission of 3D data in various applications, such as virtual reality, autonomous driving, and 3D modelling.
Bert Van hauwermeiren +2 more
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LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction
IEEE Access, 2022 A point cloud acquired through a Light Detection And Ranging (LiDAR) sensor can be illustrated as a continuous frame with a time axis. Since the frame-by-frame point cloud has a high correlation between frames, a higher compression efficiency can be ...
Junsik Kim +3 more
doaj +1 more source
Lightweight super resolution network for point cloud geometry compression [PDF]
, 2023 This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network. The proposed method involves decomposing a point cloud into a base point cloud and the interpolation patterns for reconstructing ...
Gao, Wen +3 more
core +1 more source
Point cloud geometry compression using neural implicit representations [PDF]
, 2023 openIn recent years, the increasing prominence of 3D point clouds in various applications has led to an escalating need for efficient storage and transmission methods.
MOTAMENI, AMIRHOSSEIN
core
Prioritized Transmission Control of Point Cloud Data Obtained by LIDAR Devices
IEEE Access, 2020 Smart monitoring, particularly at intersections, is a promising service that is being considered for the concept of smart cities. A network of light detection and ranging (LIDAR) sensors, which generates point cloud data in real time, can be used to ...
Keiichiro Sato +6 more
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Contextual Homogeneity-Based Patch Decomposition Method for Higher Point Cloud Compression
IEEE Access, 2020 Point cloud content is widely used to store and represent 3D volumetric objects with a complex and detailed representation from any direction of view.
Sungryeul Rhyu +3 more
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Subjective Quality Assessment of V-PCC-Compressed Dynamic Point Clouds Degraded by Packet Losses
Sensors, 2023 This article describes an empirical exploration on the effect of information loss affecting compressed representations of dynamic point clouds on the subjective quality of the reconstructed point clouds.
Emil Dumic, Luis A. da Silva Cruz
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Learned Point Cloud Geometry Compression [PDF]
, 2019 This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a.k.a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE).
Liu, Haojie +5 more
core +1 more source
Lossless Compression of Point Cloud Sequences Using Sequence Optimized CNN Models
IEEE Access, 2022 In this paper we propose a new paradigm for encoding the geometry of dense point cloud sequences, where a convolutional neural network (CNN), which estimates the encoding distributions, is optimized on several frames of the sequence to be compressed.
Emre C. Kaya, Ioan Tabus
doaj +1 more source
Joint Geometry and Color Projection-Based Point Cloud Quality Metric
IEEE Access, 2022 Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment of point cloud coding methods require reliable objective quality metrics to evaluate the level of degradation ...
Alireza Javaheri +3 more
doaj +1 more source