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DeepPCC: Learned Lossy Point Cloud Compression
IEEE Transactions on Emerging Topics in Computational IntelligenceWe propose DeepPCC, an end-to-end learning-based approach for the lossy compression of large-scale object point clouds. For both geometry and attribute components, we introduce the Multiscale Neighborhood Information Aggregation (NIA) mechanism, which ...
Junzhe Zhang +4 more
semanticscholar +2 more sources
MP3-based point cloud compression
2021 International Conference on Electronics, Information, and Communication (ICEIC), 2021Geometry-based Point Cloud Compression (G-PCC) and Video-based Point Cloud Compression (V-PCC), which are standards developed by MPEG, can be used in compressing the point cloud data. However, these standards do not yet support encoding and decoding in many devices.
Jaiyoung Oh, Euee S. Jang
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Compression of Plenoptic Point Clouds
IEEE Transactions on Image Processing, 2019Point clouds have been recently used in applications involving real-time capture and rendering of 3D objects. In a point cloud, for practical reasons, each point or voxel is usually associated with one single color along with other attributes. The region-adaptive hierarchical transform (RAHT) coder has been proposed for single-color point clouds.
Gustavo Sandri +2 more
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Quantum Point Cloud and its Compression
International Journal of Theoretical Physics, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jiang, Nan +3 more
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Point Cloud Compression: Technologies and Standardization
Ge Li, Wei Gao, Wen Gao
semanticscholar +2 more sources
Deep Learning-Based Point Cloud Compression: An In-Depth Survey and Benchmark
IEEE Transactions on Pattern Analysis and Machine IntelligenceWith the maturity of 3D capture technology, the explosive growth of point cloud data has burdened the storage and transmission process. Traditional hybrid point cloud compression (PCC) tools relying on handcrafted priors have limited compression ...
Wei Gao +5 more
semanticscholar +1 more source
suLPCC: A Novel LiDAR Point Cloud Compression Framework for Scene Understanding Tasks
IEEE Transactions on Industrial InformaticsLight detection and ranging (LiDAR) point cloud compression (LPCC) plays an important role in managing the storage, transmission, and perception of the rapidly expanding volume of LiDAR point cloud (LPC) data. However, there has been a noticeable lack of
Miaohui Wang +4 more
semanticscholar +1 more source
Real-Time LiDAR Point Cloud Compression and Transmission for Resource-Constrained Robots
IEEE International Conference on Robotics and AutomationLiDARs are widely used in autonomous robots due to their ability to provide accurate environment structural information. However, the large size of point clouds poses challenges in terms of data storage and transmission. In this paper, we propose a novel
Yuhao Cao, Yu Wang, Haoyao Chen
semanticscholar +1 more source
AAAI Conference on Artificial Intelligence
Dynamic point cloud compression (DPCC) is crucial in applications like autonomous driving and AR/VR. Current compression methods face challenges with complexity management and rate control.
Chenhao Zhang, Wei Gao
semanticscholar +1 more source
Dynamic point cloud compression (DPCC) is crucial in applications like autonomous driving and AR/VR. Current compression methods face challenges with complexity management and rate control.
Chenhao Zhang, Wei Gao
semanticscholar +1 more source