IllumiNeRF: 3D Relighting Without Inverse Rendering
Authors: Xiaoming Zhao, Pratul Srinivasan, Dor Verbin, Keunhong Park, Ricardo Martin Brualla, Philipp Henzler
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |