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..
Retrieval-Augmented Score Distillation for Text-to-3D Generation
Authors: Junyoung Seo, Susung Hong, Wooseok Jang, Inès Hyeonsu Kim, Min-Seop Kwak, Doyup Lee, Seungryong Kim
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to demonstrate that Re Dream exhibits superior quality with increased geometric consistency. |
| Researcher Affiliation | Collaboration | 1Korea Univeristy, Seoul, Korea 2Runway, New York, USA. Correspondence to: Seungryong Kim <seungryong EMAIL>, Doyup Lee <EMAIL>. |
| Pseudocode | No | The paper describes its methods using mathematical formulations and textual explanations, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project page is available at https: //ku-cvlab.github.io/Re Dream/. |
| Open Datasets | Yes | We utilize 3D assets from Objaverse 1.0 (Deitke et al., 2023b) dataset and corresponding captions with the help of Cap3D (Luo et al., 2023). |
| Dataset Splits | No | The paper focuses on optimizing 3D representations rather than traditional model training with dataset splits. No specific training, validation, or test splits are provided for model training. |
| Hardware Specification | Yes | Our experiments were conducted on an NVIDIA RTX A6000 GPU, with a total of 20,000 iterations of optimization for generation. |
| Software Dependencies | Yes | For all our experiments, Instant-NGP (M uller et al., 2022) is used for our Ne RF backbone and Stable Diffusion v2 (Rombach et al., 2022b) as the 2D prior. |
| Experiment Setup | Yes | Our experiments were conducted on an NVIDIA RTX A6000 GPU, with a total of 20,000 iterations of optimization for generation. For all our experiments, Instant-NGP (M uller et al., 2022) is used for our Ne RF backbone and Stable Diffusion v2 (Rombach et al., 2022b) as the 2D prior. For our method, we retrieve 3 assets and render our retrieved data with 100 uniformly sampled camera poses. |