Search research articles

Search research articles

Related Experiment Videos

Extreme compression and modeling of bidirectional texture function.

Michal Haindl¹, Jirí Filip

¹Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Prague. haindl@utia.cz

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 19, 2007

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Material fingerprinting: predicting human perception of material appearance through psychophysical analysis and neural networks.

Royal Society open science·2025

Same author

Perceptual dimensions of wood materials.

Journal of vision·2024

Same author

Texture Segmentation Benchmark.

IEEE transactions on pattern analysis and machine intelligence·2021

Same author

Rapid material appearance acquisition using consumer hardware.

Sensors (Basel, Switzerland)·2014

Same author

Bidirectional texture function modeling: a state of the art survey.

IEEE transactions on pattern analysis and machine intelligence·2009

Same author

Hierarchical multiple Markov chain model for unsupervised texture segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2009

Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study introduces a novel algorithm for compressing Bidirectional Texture Functions (BTFs), essential for realistic virtual reality materials. The method achieves extreme compression, enabling efficient rendering and reconstruction of material appearance.

Area of Science:

Computer Graphics
Material Science
Image Processing

Background:

Bidirectional Texture Functions (BTFs) represent realistic material appearance under varying light and viewing conditions.
BTFs require thousands of measurements, leading to large data sizes unsuitable for direct rendering.
Compression of BTF data is crucial for practical applications in virtual reality and computer graphics.

Purpose of the Study:

To present a novel, fast probabilistic algorithm for Bidirectional Texture Function (BTF) modeling and compression.
To enable realistic material rendering in virtual reality applications with significantly reduced data size.
To achieve extreme compression ratios for BTF data, allowing for hardware implementation and reconstruction of missing data.

Main Methods:

Related Experiment Videos

BTF space segmentation and range map estimation using photometric stereo.

Spectral and spatial factorization of selected sub-space color texture images.

Independent spatial probabilistic modeling of mono-spectral band-limited factors.

Synthesis of sub-space images and combination of color/range information for bump-mapping.

Main Results:

A novel probabilistic model-based algorithm for realistic BTF modeling.
Achieved unprecedented BTF compression ratios, surpassing alternative sampling-based methods.
Demonstrated the possibility of fast hardware implementation for BTF rendering.
Enabled reconstruction of missing parts within the BTF measurement space.

Conclusions:

The proposed algorithm offers a highly efficient method for compressing and rendering Bidirectional Texture Functions.
The technique allows for creating visual material impressions without pixel-wise correspondence to original measurements.
This approach facilitates realistic material representation in virtual reality with significantly reduced computational and storage demands.