312 research outputs found
Adaptive coarse-to-fine quantization for optimizing rate-distortion of progressive mesh compression
International audienceWe propose a new connectivity-based progressivecompression approach for triangle meshes. The keyidea is to adapt the quantization precision to the resolutionof each intermediate mesh so as to optimizethe rate-distortion trade-off. This adaptation is automaticallydetermined during the encoding processand the overhead is efficiently encoded using geometricalprediction techniques. We also introducean optimization of the geometry coding by usinga bijective discrete rotation. Results show that ourapproach delivers a better rate-distortion behaviorthan both connectivity-based and geometry-basedcompression state of the art method
Cost-driven framework for progressive compression of textured meshes
International audienceRecent advances in digitization of geometry and radiometry generate in routine massive amounts of surface meshes with texture or color attributes. This large amount of data can be compressed using a progressive approach which provides at decoding low complexity levels of details (LoDs) that are continuously refined until retrieving the original model. The goal of such a progressive mesh compression algorithm is to improve the overall quality of the transmission for the user, by optimizing the rate-distortion trade-off. In this paper, we introduce a novel meaningful measure for the cost of a progressive transmission of a textured mesh by observing that the rate-distortion curve is in fact a staircase, which enables an effective comparison and optimization of progressive transmissions in the first place. We contribute a novel generic framework which utilizes the cost function to encode triangle surface meshes via multiplexing several geometry reduction steps (mesh decimation via half-edge or full-edge collapse operators, xyz quantization reduction and uv quantization reduction). This framework can also deal with textures by multiplexing an additional texture reduction step. We also design a texture atlas that enables us to preserve texture seams during decimation while not impairing the quality of resulting LODs. For encoding the inverse mesh decimation steps we further contribute a significant improvement over the state-of-the-art in terms of rate-distortion performance and yields a compression-rate of 22:1, on average. Finally, we propose a unique single-rate alternative solution using a selection scheme of a subset among LODs, optimized for our cost function, and provided with our atlas that enables interleaved progressive texture refinements
Coarse-grained Multiresolution Structures for Mobile Exploration of Gigantic Surface Models
We discuss our experience in creating scalable systems for distributing
and rendering gigantic 3D surfaces on web environments and
common handheld devices. Our methods are based on compressed
streamable coarse-grained multiresolution structures. By combining
CPU and GPU compression technology with our multiresolution
data representation, we are able to incrementally transfer, locally
store and render with unprecedented performance extremely
detailed 3D mesh models on WebGL-enabled browsers, as well as
on hardware-constrained mobile devices
Optimizing Error-Bounded Lossy Compression for Three-Dimensional Adaptive Mesh Refinement Simulations
Today's scientific simulations require a significant reduction of data volume
because of extremely large amounts of data they produce and the limited I/O
bandwidth and storage space. Error-bounded lossy compression has been
considered one of the most effective solutions to the above problem. However,
little work has been done to improve error-bounded lossy compression for
Adaptive Mesh Refinement (AMR) simulation data. Unlike the previous work that
only leverages 1D compression, in this work, we propose to leverage
high-dimensional (e.g., 3D) compression for each refinement level of AMR data.
To remove the data redundancy across different levels, we propose three
pre-process strategies and adaptively use them based on the data
characteristics. Experiments on seven AMR datasets from a real-world
large-scale AMR simulation demonstrate that our proposed approach can improve
the compression ratio by up to 3.3X under the same data distortion, compared to
the state-of-the-art method. In addition, we leverage the flexibility of our
approach to tune the error bound for each level, which achieves much lower data
distortion on two application-specific metrics.Comment: 13 pages, 17 figures, 3 tables, accepted by ACM HPDC 202
Network streaming and compression for mixed reality tele-immersion
Bulterman, D.C.A. [Promotor]Cesar, P.S. [Copromotor
Compression of dynamic polygonal meshes with constant and variable connectivity
This work was supported by the projects 20-02154S and 17-07690S of the Czech
Science Foundation and SGS-2019-016 of the Czech Ministry of Education.Polygonal mesh sequences with variable connectivity are incredibly versatile dynamic surface representations as they allow a surface to change topology or details
to suddenly appear or disappear. This, however, comes at the cost of large storage size. Current compression methods inefficiently exploit the temporal coherence
of general data because the correspondences between two subsequent frames might
not be bijective. We study the current state of the art including the special class of
mesh sequences for which connectivity is static. We also focus on the state of the
art of a related field of dynamic point cloud sequences. Further, we point out parts
of the compression pipeline with the possibility of improvement. We present the
progress we have already made in designing a temporal model capturing the temporal coherence of the sequence, and point out to directions for a future research
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