Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives
Authors: Tom Monnier, Jake Austin, Angjoo Kanazawa, Alexei Efros, Mathieu Aubry
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |