Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing
Authors: Antoine Scardigli, Lukas Cavigelli, Lorenz K. Müller
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3.1 Experimental Setup, 3.2 Quantitative Results, 3.3 Qualitative Results, Table 1: Description, visual quality evaluation at 4.0 spp budget, and inference time in ms. |
| Researcher Affiliation | Industry | Lukas Cavigelli, Lorenz K. Müller Computing Systems Lab, Huawei Zurich Research Center, Switzerland and EMAIL |
| Pseudocode | No | The paper describes the algorithms for its three models (Sampling Importance Network, Latent Encoder Network, Denoiser) in Section 2.3, but it does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | Yes | We release our dataset scenes and code implementation1 to facilitate the usage and comparison of our method. 1Source code available at https://github.com/AJSVB/RL_PATH_TRACING. |
| Open Datasets | Yes | We use three 3D scenes Emerald Square [40], Sun Temple [41], and Zero-Day [42] released as part of the Open Research Content Archive (ORCA) under CC BY 4.0 license. |
| Dataset Splits | No | The paper mentions 'cross-validated results for each scene: For every test scene, we train on all the other scenes', which describes a cross-validation strategy but does not specify explicit training/validation/test dataset splits (percentages or counts) for a single dataset. |
| Hardware Specification | Yes | We perform our measurements on a Nvidia Ge Force RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper states 'We use Adam optimizer [38], Pytorch and Ray-RLlib [39]' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | All networks are trained in a closed loop on 100 epochs with a batch size of 4. ... the learning rate has a maximum value of 0.1 and a minimum value of 10 8, with a warmup phase of 15%, and then an exponential decay phase. ... We transform the images by using vertical and/or horizontal flips, by randomly cropping and rescaling the images. |