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Photon-Driven Neural Reconstruction for Path Guiding
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-01252-6_6openalex-snapshot
Image Inpainting for Irregular Holes Using Partial Convolutions
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
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/10.1007/978-3-7091-6303-0_2openalex-snapshot
Metropolis Light Transport for Participating Media
inferredcites →https://doi.org/10.1007/978-3-7091-9430-0_31openalex-snapshot
Importance Driven Path Tracing using the Photon Map
inferredcites →https://doi.org/10.1016/j.cag.2020.09.007openalex-snapshot
Deep radiance caching: Convolutional autoencoders deeper in ray tracing
inferredcites →https://doi.org/10.1109/cvpr.1993.341010openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00753openalex-snapshot
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
inferredcites →https://doi.org/10.1109/iccv.1995.466933openalex-snapshot
Proceedings of IEEE International Conference on Computer Vision
inferredcites →https://doi.org/10.1109/iccv.2015.164openalex-snapshot
Holistically-Nested Edge Detection
inferredcites →https://doi.org/10.1109/jproc.2020.2976475openalex-snapshot
Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/msp.2017.2765695openalex-snapshot
Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges
inferredcites →https://doi.org/10.1111/cgf.13227openalex-snapshot
Practical Path Guiding for Efficient Light‐Transport Simulation
inferredcites →https://doi.org/10.1111/cgf.13858openalex-snapshot
Offline Deep Importance Sampling for Monte Carlo Path Tracing
inferredcites →https://doi.org/10.1111/cgf.14051openalex-snapshot
Practical Product Path Guiding Using Linearly Transformed Cosines
inferredcites →https://doi.org/10.1111/cgf.14052openalex-snapshot
Deep Kernel Density Estimation for Photon Mapping
inferredcites →https://doi.org/10.1145/1618452.1618487openalex-snapshot
Stochastic progressive photon mapping
inferredcites →https://doi.org/10.1145/1661412.1618487openalex-snapshot
Stochastic progressive photon mapping
inferredcites →https://doi.org/10.1145/218380.218498openalex-snapshot
Optimally combining sampling techniques for Monte Carlo rendering
inferredcites →https://doi.org/10.1145/2601097.2601203openalex-snapshot
On-line learning of parametric mixture models for light transport simulation
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/10.1145/3130800.3130891openalex-snapshot
Learning to predict indoor illumination from a single image
inferredcites →https://doi.org/10.1145/3197517.3201313openalex-snapshot
Deep image-based relighting from optimal sparse samples
inferredcites →https://doi.org/10.1145/3197517.3201388openalex-snapshot
Denoising with kernel prediction and asymmetric loss functions
inferredcites →https://doi.org/10.1145/3272127.3275030openalex-snapshot
Selective guided sampling with complete light transport paths
inferredcites →https://doi.org/10.1145/3368313openalex-snapshot
Adaptive Incident Radiance Field Sampling and Reconstruction Using Deep Reinforcement Learning
inferredcites →https://doi.org/10.1145/3386569.3392421openalex-snapshot
Robust fitting of parallax-aware mixtures for path guiding
inferredcites →https://doi.org/10.1145/3450626.3459810openalex-snapshot
Hierarchical neural reconstruction for path guiding using hybrid path and photon samples
inferredcites →https://doi.org/10.1162/neco.1991.3.1.79openalex-snapshot
Adaptive Mixtures of Local Experts
inferredcites →https://doi.org/10.2312/sre.20181174openalex-snapshot
Primary Sample Space Path Guiding
inferredcites →https://doi.org/10.5555/3071773.3071781openalex-snapshot
Product Importance Sampling for Light Transport Path Guiding
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-50072-5_15openalex-snapshot
Enhanced Direct Lighting Using Visibility-Aware Light Sampling
inferred← citeshttps://doi.org/10.1016/j.cag.2024.103945openalex-snapshot
Path guiding for wavefront path tracing: A memory efficient approach for GPU path tracers
inferred← citeshttps://doi.org/10.1109/tvcg.2023.3259382openalex-snapshot
NeRC: Rendering Planar Caustics by Learning Implicit Neural Representations
inferred← citeshttps://doi.org/10.1111/cgf.14675openalex-snapshot
Real‐time Deep Radiance Reconstruction from Imperfect Caches
inferred← citeshttps://doi.org/10.1111/cgf.14997openalex-snapshot
Neural Path Sampling for Rendering Pure Specular Light Transport
inferred← citeshttps://doi.org/10.1111/cgf.70050openalex-snapshot
Neural Two‐Level Monte Carlo Real‐Time Rendering
inferred← citeshttps://doi.org/10.1145/3618360openalex-snapshot
Manifold Path Guiding for Importance Sampling Specular Chains
inferred← citeshttps://doi.org/10.1145/3721238.3730754openalex-snapshot
Neural Importance Sampling of Many Lights
inferred← citeshttps://doi.org/10.1145/3757377.3763813openalex-snapshot
ReSTIR PG: Path Guiding with Spatiotemporally Resampled Paths
inferred← citeshttps://doi.org/10.1145/3768618openalex-snapshot
A Survey on Deep Learning for Monte Carlo Path Tracing
inferred← citeshttps://doi.org/10.2316/j.2024.203-0502openalex-snapshot
APPLICATION OF HYBRID INTELLIGENT MULTI-OBJECTIVE LIGHTING ALGORITHMS IN HOUSING DESIGN, 1-6.
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