We show how spectral methods may be applied to 3D mesh data to obtain compact representations. This is achieved by projecting the mesh geometry onto an orthonormal basis derived from the mesh topology. To reduce complexity, the mesh is partitioned into a number of balanced submeshes with minimal interaction, each of which are compressed independently. Our methods may be used for compression and progressive transmission of 3D content, and are shown to be vastly superior to existing methods using spatial techniques, if slight loss can be tolerated.

/pdf/spectral-compression-of-mesh-geometry-4idbrb9zid.pdf

Spectral compression of mesh geometry

Lossy coding of speech, high-quality audio, still images, and video is commonplace today. However, in 1948, few lossy compression systems were in service. Shannon introduced and developed the theory of source coding with a fidelity criterion, also called rate-distortion theory. For the first 25 years of its existence, rate-distortion theory had relatively little impact on the methods and systems actually used to compress real sources. Today, however, rate-distortion theoretic concepts are an important component of many lossy compression techniques and standards. We chronicle the development of rate-distortion theory and provide an overview of its influence on the practice of lossy source coding.

Lossy source coding

Since Shapiro (see ibid., vol.41, no.12, p. 445, 1993) published his work on embedded zerotree wavelet (EZW) image coding, there have been increased research activities in image coding centered around wavelets. We first point out that the wavelet transform is just one member in a family of linear transformations, and the discrete cosine transform (DCT) can also be coupled with an embedded zerotree quantizer. We then present such an image coder that outperforms any other DCT-based coder published in the literature, including that of the Joint Photographers Expert Group (JPEG). Moreover, our DCT-based embedded image coder gives higher peak signal-to-noise ratios (PSNR) than the quoted results of Shapiro's EZW coder.

A DCT-based embedded image coder

3D meshes are commonly used to represent virtual surface and volumes. However, their raw data representations take a large amount of space. Hence, 3D mesh compression has been an active research topic since the mid 1990s. In 2005, two very good review articles describing the pioneering works were published. Yet, new technologies have emerged since then. In this article, we summarize the early works and put the focus on these novel approaches. We classify and describe the algorithms, evaluate their performance, and provide synthetic comparisons. We also outline the emerging trends for future research.

3D Mesh Compression: Survey, Comparisons, and Emerging Trends

A system for coding video data comprised of one or more frames codes a portion of the video data using a frame-prediction coding technique, and generates residual images based on the video data and the coded video data. The system then codes the residual images using a fine-granular scalability coding technique, and outputs the coded video data and at least one of the coded residual images to a receiver.

Scalable video coding system

With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress. However, it remains a great challenge to accurately extract disparities from real-world image pairs taken by consumer-level devices like smartphones, due to practical complicating factors such as thin structures, non-ideal rectification, camera module inconsistencies and various hard-case scenes. In this paper, we propose a set of innovative designs to tackle the problem of practical stereo matching: 1) to better recover fine depth details, we design a hierarchical network with recurrent refinement to update disparities in a coarse-to-fine manner, as well as a stacked cascaded architecture for inference; 2) we propose an adaptive group correlation layer to mitigate the impact of erroneous rectification; 3) we introduce a new synthetic dataset with special attention to difficult cases for better generalizing to real-world scenes. Our results not only rank 1st on both Middlebury and ETH3D benchmarks, outperforming existing state-of-the-art methods by a notable margin, but also exhibit high-quality details for real-life photos, which clearly demonstrates the efficacy of our contributions.

Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation

The triangular mesh provides one of the most popular representations for 3D graphic models. A typical triangular mesh consists of two different types of data: topological data which specify the connectivity of the mesh and geometrical data which describe information associated with each individual vertex or triangle. We propose a new compression scheme which encode topological data by using the dual graph of the original mesh. It is found that the dual graph can be represented as a degraded binary tree. Furthermore, geometrical data can be coded progressively with local prediction and embedded entropy coding. Experimental results show that an acceptable quality level can be reached at a compression ratio of 60 to 1 for general test models.

A dual graph approach to 3D triangular mesh compression

Motivated by Shapiro's (1993) embedded zerotree wavelet (EZW) coding and Taubman and Zakhor's (1994) layered zero coding (LZC), we propose a layered discrete cosine transform (DCT) image compression scheme, which generates an embedded bit stream for DCT coefficients according to their importance. The new method allows progressive image transmission and simplifies the rate-control problem. In addition to these functionalities, it provides a substantial rate-distortion improvement over the JPEG standard when the bit rates become low. For example, we observe a bit rate reduction with respect to the JPEG Huffman and arithmetic coders by about 60% and 20%, respectively, for a bit rate around 0.1 b/p.

/pdf/layered-dct-still-image-compression-2oteiwq7eu.pdf

Layered DCT still image compression

Motivated by Shapiro's (see IEEE Trans. on Signal Processing, vol.41, no.12, p. 3445-62, 1993) embedded zerotree wavelet coding (EZW) and Taubman and Zakhor's (see IEEE Trans. on Image Processing, vol.3, no.3, p.572-88, 1994) layered zero coding (LZC), we propose a layered DCT image compression scheme, which generates an embedded bit stream for DCT coefficients according to their importance. The new method allows progressive image transmission and simplifies the rate-control problem. In addition to these functionalities, it provides a substantial rate-distortion improvement over the JPEG standard when the bit rates become low. For example, we observe a bit rate reduction with respect to the JPEG Huffman and arithmetic coders by about 60% and 20%, respectively, for a bit rate around 0.1 bpp.

An embedded DCT approach to progressive image compression

A progressive compression method which encodes a 3D graphic models into an embedded bit stream is investigated. The coder first encodes the coarsest resolution of the model, and then includes the information of finer details gradually. A rate-distortion model is used in the integration of different types of bit streams into a single embedded bit stream to optimize the overall coding performance. This embedding property can be applied to progressive transmission, multi-resolution editing and level-of-detail control. Numerical experiments are provided to demonstrate the excellent rate-distortion performance of the proposed method.

Embedded coding of 3D graphic models

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jiankun Li

Author Tools

Chat about Author