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Neural Denoising for Path Tracing of Medical Volumetric Data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Practical Global Illumination with Irradiance Caching
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U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-642-87825-1openalex-snapshot
Photorealistic Rendering Techniques
inferredcites →https://doi.org/10.1007/s13244-016-0518-1openalex-snapshot
Cinematic rendering – an alternative to volume rendering for 3D computed tomography imaging
inferredcites →https://doi.org/10.1016/j.media.2016.06.016openalex-snapshot
Shaping the future through innovations: From medical imaging to precision medicine
inferredcites →https://doi.org/10.1109/cvpr.2019.00223openalex-snapshot
Noise2Void - Learning Denoising From Single Noisy Images
inferredcites →https://doi.org/10.1109/tci.2016.2644865openalex-snapshot
Loss Functions for Image Restoration With Neural Networks
inferredcites →https://doi.org/10.1109/tip.2003.819861openalex-snapshot
Image quality assessment: from error visibility to structural similarity
inferredcites →https://doi.org/10.1109/tmi.2010.2090538openalex-snapshot
MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning
inferredcites →https://doi.org/10.1111/cgf.12252openalex-snapshot
A Survey of Volumetric Illumination Techniques for Interactive Volume Rendering
inferredcites →https://doi.org/10.1145/218380.218498openalex-snapshot
Optimally combining sampling techniques for Monte Carlo rendering
inferredcites →https://doi.org/10.1145/2366145.2366213openalex-snapshot
SURE-based optimization for adaptive sampling and reconstruction
inferredcites →https://doi.org/10.1145/2366145.2366214openalex-snapshot
Adaptive rendering with non-local means filtering
inferredcites →https://doi.org/10.1145/2461912.2461947openalex-snapshot
Axis-aligned filtering for interactive physically-based diffuse indirect lighting
inferredcites →https://doi.org/10.1145/2641762openalex-snapshot
Adaptive Rendering Based on Weighted Local Regression
inferredcites →https://doi.org/10.1145/2766977openalex-snapshot
A machine learning approach for filtering Monte Carlo noise
inferredcites →https://doi.org/10.1145/3072959.3073601openalex-snapshot
Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder
inferredcites →https://doi.org/10.1145/3072959.3073708openalex-snapshot
Kernel-predicting convolutional networks for denoising Monte Carlo renderings
inferredcites →https://doi.org/10.1145/3105762.3105770openalex-snapshot
Spatiotemporal variance-guided filtering
inferredcites →https://doi.org/10.1145/3197517.3201388openalex-snapshot
Denoising with kernel prediction and asymmetric loss functions
inferredcites →https://doi.org/10.1145/3269978openalex-snapshot
Blockwise Multi-Order Feature Regression for Real-Time Path-Tracing Reconstruction
inferredcites →https://doi.org/10.1145/3306346.3322954openalex-snapshot
Sample-based Monte Carlo denoising using a kernel-splatting network
inferredcites →https://doi.org/10.1145/3306346.3323038openalex-snapshot
Deep convolutional reconstruction for gradient-domain rendering
inferredcites →https://doi.org/10.1145/3355088.3365150openalex-snapshot
Faster RPNN: Rendering Clouds with Latent Space Light Probes
inferredcites →https://doi.org/10.1145/3355089.3356492openalex-snapshot
Learning efficient illumination multiplexing for joint capture of reflectance and shape
inferredcites →https://doi.org/10.1145/360825.360839openalex-snapshot
Illumination for computer generated pictures
inferredcites →https://doi.org/10.1201/b10685openalex-snapshot
Realistic Image Synthesis Using Photon Mapping
inferredcites →https://doi.org/10.48550/arxiv.1709.05418openalex-snapshot
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-20713-6_8openalex-snapshot
Deep Learning Based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering
inferred← citeshttps://doi.org/10.1007/978-3-031-40286-9_19openalex-snapshot
Monte Carlo Medical Volume Rendering Denoising via Auxiliary Feature Guided Self-attention and Convolution Integrated
inferred← citeshttps://doi.org/10.1007/s41095-021-0209-9openalex-snapshot
A survey on deep learning-based Monte Carlo denoising
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110979openalex-snapshot
Advancements in biomedical rendering: A survey on AI-based denoising techniques
inferred← citeshttps://doi.org/10.1016/j.visinf.2026.100323openalex-snapshot
Advances in AI-powered photorealistic ultrasound visualization techniques: A survey
inferred← citeshttps://doi.org/10.1109/tvcg.2022.3209418openalex-snapshot
Quick Clusters: A GPU-Parallel Partitioning for Efficient Path Tracing of Unstructured Volumetric Grids
inferred← citeshttps://doi.org/10.1109/tvcg.2022.3209498openalex-snapshot
FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks
inferred← citeshttps://doi.org/10.1109/tvcg.2024.3445339openalex-snapshot
Real-Time Realistic Volume Rendering of Consistently High Quality With Dynamic Illumination
inferred← citeshttps://doi.org/10.1109/tvcg.2025.3549882openalex-snapshot
Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization
inferred← citeshttps://doi.org/10.1109/tvcg.2026.3653310openalex-snapshot
Materializing Inter-Channel Relationships With Multi-Density Woodcock Tracking
inferred← citeshttps://doi.org/10.1109/vr58804.2024.00123openalex-snapshot
Immersive 3D Medical Visualization in Virtual Reality using Stereoscopic Volumetric Path Tracing
inferred← citeshttps://doi.org/10.1109/vr67842.2026.00078openalex-snapshot
Hybrid Foveated Path Tracing with Peripheral Gaussians for Immersive Anatomy
inferred← citeshttps://doi.org/10.1111/cgf.14587openalex-snapshot
Automatic Feature Selection for Denoising Volumetric Renderings
inferred← citeshttps://doi.org/10.1111/cgf.14811openalex-snapshot
Memory‐Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh
inferred← citeshttps://doi.org/10.1111/cgf.14857openalex-snapshot
State‐of‐the‐art in Large‐Scale Volume Visualization Beyond Structured Data
inferred← citeshttps://doi.org/10.1111/cgf.70276openalex-snapshot
Real‐time Neural Denoising for Volume Rendering Using Dual‐Input Feature Fusion Network
inferred← citeshttps://doi.org/10.1145/3451256openalex-snapshot
Interactive Path Tracing and Reconstruction of Sparse Volumes
inferred← citeshttps://doi.org/10.1145/3585497openalex-snapshot
Joint Neural Denoising of Surfaces and Volumes
inferred← citeshttps://doi.org/10.1145/3675384openalex-snapshot
Converging Algorithm-Agnostic Denoising for Monte Carlo Rendering
inferred← citeshttps://doi.org/10.1145/3687949openalex-snapshot
Neural Kernel Regression for Consistent Monte Carlo Denoising
inferred← citeshttps://doi.org/10.1145/3768618openalex-snapshot
A Survey on Deep Learning for Monte Carlo Path Tracing
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4512330/v1openalex-snapshot
Real-Time Spatiotemporal Denoising Volumetric Rendering in Three-Dimensional Visualization of Puncturing Navigation
inferred← citeshttps://doi.org/10.3390/app15189893openalex-snapshot
Deep Learning-Based Denoising for Interactive Realistic Rendering of Biomedical Volumes
inferred← citeshttps://doi.org/10.3390/jimaging11040126openalex-snapshot
Real-Time Volume-Rendering Image Denoising Based on Spatiotemporal Weighted Kernel Prediction
inferred← citeshttps://doi.org/10.3390/jimaging11060193openalex-snapshot
Efficient Multi-Material Volume Rendering for Realistic Visualization with Complex Transfer Functions
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