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..
Sparse3D: Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views
Authors: Zixin Zou, Weihao Cheng, Yan-Pei Cao, Shi-Sheng Huang, Ying Shan, Song-Hai Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on CO3DV2 which is a multi-view dataset of real-world objects. Both quantitative and qualitative evaluations demonstrate that our approach outperforms previous state-of-the-art works on the metrics regarding NVS and geometry reconstruction. |
| Researcher Affiliation | Collaboration | Zixin Zou1, Weihao Cheng2, Yan-Pei Cao2, Shi-Sheng Huang3, Ying Shan2, Song-Hai Zhang1 1BNRist, Tsinghua University 2ARC Lab, Tencent PCG 3Beijing Normal University |
| Pseudocode | No | The paper describes its methods through text and diagrams (e.g., Figure 2 and 3) but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Ar Xiv version with supplementary materials is available at https://arxiv.org/abs/2308.14078 |
| Open Datasets | Yes | We conduct experiments on CO3DV2 which is a multi-view dataset of real-world objects. We follow the fewview-train and fewview-dev splits provided by CO3Dv2 dataset (Reizenstein et al. 2021) for training and evaluation purposes, respectively. |
| Dataset Splits | Yes | We follow the fewview-train and fewview-dev splits provided by CO3DV2 dataset (Reizenstein et al. 2021) for training and evaluation purposes, respectively. |
| Hardware Specification | Yes | Ne RF optimization runs for 10,000 steps, which takes about 45 minutes on a single 3090 GPU. |
| Software Dependencies | Yes | For the multiview-consistent model, we adopt the Stable Diffusion model v1.5 as our priors. For Ne RF reconstruction, we adapt the threestudio (Guo et al. 2023), which is a unified framework for 3D content creation from various inputs, to implement the Ne RF reconstruction for specific objects. |
| Experiment Setup | Yes | We set the weights of the losses with λp = 100, λc = 10, λr = 1000 and λm = 50. Ne RF optimization runs for 10,000 steps, which takes about 45 minutes on a single 3090 GPU. In our experiment with setting the CFG value as 7.5 |