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..
Disentangled 3D Scene Generation with Layout Learning
Authors: Dave Epstein, Ben Poole, Ben Mildenhall, Alexei A Efros, Aleksander Holynski
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We examine the ability of layout learning to generate and disentangle 3D scenes across a wide range of text prompts. We first verify our method s effectiveness through an ablation study and comparison to baselines, and then demonstrate various applications enabled by layout learning. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, UC Berkeley 2Google Research. |
| Pseudocode | Yes | Figure 9: Pseudocode for layout learning, with segments inherited from previous work abstracted into functions. Figure 10: Pseudocode for empty Ne RF regularization, where soft_bin_acc computes αbin in Equation 5. |
| Open Source Code | No | The paper provides a project page link (https://dave.ml/layoutlearning/) but does not explicitly state that the source code for the described methodology is available there or elsewhere. |
| Open Datasets | No | The paper uses a pretrained text-to-image diffusion model (Imagen) and a list of prompts for generation and evaluation, but does not specify a publicly available dataset they used for training their own models from scratch or provide access information for any dataset explicitly used for their experiments beyond the prompts. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Mip-Ne RF 360, Imagen, and Shampoo, but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | Yes | We use λdist = 0.001, λacc = 0.01, λori = 0.01 as well as λempty = 0.05. We initialize parameters s N(1, 0.3), t(i) N(0, 0.3), and q(i) N(µi, 0.1) where µi is 1 for the last element and 0 for all others. We use a 10 higher learning rate to train layout parameters. We use a classifier-free guidance strength of 200 and textureless shading probability of 0.1 for SDS (Poole et al., 2022), disabling view-dependent prompting as it does not aid in the generation of compositional scenes (Table 3b). We optimize our model with Shampoo (Gupta et al., 2018) with a batch size of 1 for 15000 steps with an annealed learning rate, starting from 10 9, peaking at 10 4 after 3000 steps, and decaying to 10 6. |