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..
Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives
Authors: Tom Monnier, Jake Austin, Angjoo Kanazawa, Alexei Efros, Mathieu Aubry
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Academia | 1LIGM, Ecole des Ponts, Univ Gustave Eiffel 2UC Berkeley |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and video results are available at www.tmonnier.com/DBW. |
| Open Datasets | Yes | DTU [25] is an MVS dataset containing 80 forward-facing scenes... We evaluate on 10 scenes (S24, S31, S40, S45, S55, S59, S63, S75, S83, S105)... The first row corresponds to the Campanile scene from Nerfstudio repository [66] and the last four rows correspond to Blended MVS scenes [75]. |
| Dataset Splits | No | The paper describes using all available views for each scene (49 or 64 depending on the scenes) from the DTU dataset, but does not specify explicit training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | Optimizing our model on a scene roughly takes 4 hours on a single NVIDIA RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions using PyTorch3D framework but does not provide specific version numbers for it or other key software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Our model is optimized using Adam [31] with a batch size of 4 for roughly a total of 25k iterations. We use learning rates of 0.05 for the texture images and 0.005 for all other parameters, and divide them by 10 for the last 2k iterations. |