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..
IllumiNeRF: 3D Relighting Without Inverse Rendering
Authors: Xiaoming Zhao, Pratul Srinivasan, Dor Verbin, Keunhong Park, Ricardo Martin Brualla, Philipp Henzler
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that this strategy is surprisingly competitive and achieves state-of-the-art results on multiple relighting benchmarks. Please see our project page at illuminerf.github.io. ... We evaluate our method on two datasets: Tenso IR [23], a synthetic benchmark, and Stanford-ORB [27], a real-world benchmark. |
| Researcher Affiliation | Collaboration | Xiaoming Zhao1,3 Pratul P. Srinivasan2 Dor Verbin2 Keunhong Park1 Ricardo Martin-Brualla1 Philipp Henzler1 1Google Research 2Google DeepMind 3University of Illinois Urbana-Champaign |
| Pseudocode | No | The paper describes its method in prose and through diagrams, but does not include any formal pseudocode blocks or algorithms. |
| Open Source Code | No | We have not made the code or model weights available online, however, the Objaverse dataset is publicly available as well as the datasets required for the Stanford-ORB and Tenso IR benchmarks. |
| Open Datasets | Yes | Relighting Dataset We render objects from Objaverse [13] under varying poses and illuminations. ... We use Objaverse [13] as the synthetic dataset. |
| Dataset Splits | No | The paper describes training and evaluation splits for Tenso IR and Stanford-ORB datasets but does not explicitly mention a separate "validation" split with specific percentages or counts. |
| Hardware Specification | Yes | Ours 29.709 0.947 0.072 0.75 h + 1 h + 0.75 h 16 A100 40GB + a TPUv5 ... Ours (single GPU) 29.245 0.946 0.073 2 h + 1 h + 2 h a A100 40GB + a TPUv5 |
| Software Dependencies | No | The paper mentions software like JAX, Stable Diffusion, Control Net, CLIP, Blender Cycles, Kubric, and Adam, but does not provide specific version numbers for any of these dependencies. |
| Experiment Setup | Yes | We decay our learning rate logarithmically from 5 10 3 to 5 10 4 over 25k training iterations with cosine-scheduled warmup in the first 500 steps. ... We fine-tune the base model for 150k steps using batch size of 512 examples and a learning rate of 10 4, which is linearly warmed up from 0 over the first 1k steps. |