Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesis
Authors: Liang Han, Junsheng Zhou, Yu-Shen Liu, Zhizhong Han
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 26,875 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,587 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the LLFF, DTU, and Blender datasets demonstrate that our method significantly outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | School of Software, Tsinghua University, Beijing, China1, Department of Computer Science, Wayne State University, Detroit, USA2 |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project page is available at: https://hanl2010.github.io/Binocular3DGS/. The source code will be publicly available. |
| Open Datasets | Yes | We conduct experiments on three public datasets, including the LLFF dataset [27], the DTU dataset [18] and the Ne RF Blender Synthetic dataset (Blender) [28]. |
| Dataset Splits | Yes | Following prior works [30, 54, 17], we used 3, 6, and 9 views as training sets for the LLFF and DTU datasets, and 8 images for training on the Blender dataset. The supplementary materials include detailed information on data splits. |
| Hardware Specification | Yes | We train our model on an RTX 3090 GPU |
| Software Dependencies | No | The paper mentions software like PDC-Net+ and LoFTR, but does not provide specific version numbers for general software dependencies (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | For the LLFF and DTU datasets, we utilize a pre-trained matching network PDC-Net+ [42] to obtain keypoints from input images...we train the LLFF and DTU datasets for 30,000 iterations, while the Blender dataset is trained for 7,000 iterations...The maximum distance dmax for camera shift is set to 0.4, the opacity decay coefficient λ is set to 0.995, and the β in the loss function 7 is set to 0.2 as in the original 3DGS [19]. |