Skip to content
DraconiticResearch
Search Sources API
197,688 studies31 sourcesUpdated 52 min ago
Peer-reviewedCC BY 4.0Mathematics

A Voxel-Based Adaptive Exposure Model for Physically Based Volume Rendering

  1. 1School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China

Abstract

In physically based volume rendering of volumetric medical imaging data, exposure levels are typically set empirically. However, differences in voxel values and opacity across transfer functions can make a fixed setting produce underexposed or overexposed results. To address this issue, we propose a voxel-contribution-based adaptive exposure model for physically based volume rendering. During volume integration, the method visibility-weights effective volumetric-sample energies using accumulated transmittance and opacity and estimates a global exposure coefficient from their weighted geometric mean. Compared with a two-dimensional HDR method operating on the rendered image, the proposed method incorporates three-dimensional information from volume integration into exposure estimation. The mean exposure-coefficient deviation is approximately 0.005%, compared with 0.031% for the two-dimensional method, while output mean-luminance variation remains below 1.10% over the tested range of normal illumination. The results demonstrate stable exposure and structural response across the evaluated transfer functions and illumination conditions.

Related

  1. An adaptive voxel-wise iterative satellite interference localization method based on a double-layer UCA antenna
    Han Li, Jinjie Yao, Muhua Li +4
    PLOS OnePeer-reviewedCC BYFull textAlgebra & number theoryProbabilityPLOSEurope PMC
  2. A stable rank-adaptive step-and-truncate finite volume method for Vlasov transport on domains with piecewise linear boundaries
    André Uschmajew, Andreas Zeiser
    arXivPreprintarXiv licenceFull textNumerical analysis & scientific computingarXiv
  3. Randomized Adaptive Sequential Allocation and the Limits of Bounded Exposure to Inferior Treatments
    Sampurna Kundu, Monitirtha Dey, Atanu Biswas
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  4. A Physically-Motivated Stochastic Model of Tropical Cyclone Dynamic Wind Fields
    Matthew LeDuc, William Kleiber
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  5. PINN-LPCDD: Physics-Informed Neural Network with Learnable and Physically Consistent Domain Decomposition for Compressible Multi-Medium Flows
    Weiheng Zeng, Hao Wang, Jiaxin Zhu +3
    arXivPreprintarXiv licenceFull textNumerical analysis & scientific computingComputational physicsarXiv
BibTeX
@article{yuan2026voxelbased,
  title = {{A Voxel-Based Adaptive Exposure Model for Physically Based Volume Rendering}},
  author = {Yuliang Yuan and Jinzhu Yang and Qi Sun and Yan Huang},
  journal = {Mathematics},
  year = {2026},
  month = nov,
  doi = {10.3390/math14214039},
  url = {https://www.mdpi.com/2227-7390/14/21/4039},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A Voxel-Based Adaptive Exposure Model for Physically Based Volume Rendering
AU  - Yuliang Yuan
AU  - Jinzhu Yang
AU  - Qi Sun
AU  - Yan Huang
PY  - 2026
DA  - 2026/11/04
JO  - Mathematics
DO  - 10.3390/math14214039
UR  - https://www.mdpi.com/2227-7390/14/21/4039
AB  - In physically based volume rendering of volumetric medical imaging data, exposure levels are typically set empirically. However, differences in voxel values and opacity across transfer functions can make a fixed setting produce underexposed or overexposed results. To address this issue, we propose a voxel-contribution-based adaptive exposure model for physically based volume rendering. During volume integration, the method visibility-weights effective volumetric-sample energies using accumulated transmittance and opacity and estimates a global exposure coefficient from their weighted geometric mean. Compared with a two-dimensional HDR method operating on the rendered image, the proposed method incorporates three-dimensional information from volume integration into exposure estimation. The mean exposure-coefficient deviation is approximately 0.005%, compared with 0.031% for the two-dimensional method, while output mean-luminance variation remains below 1.10% over the tested range of normal illumination. The results demonstrate stable exposure and structural response across the evaluated transfer functions and illumination conditions.
SN  - 2227-7390
ER  -