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.
Hybrid Mesh-Gaussian Representation for Efficient Indoor Scene Reconstruction
Authors: Binxiao Huang, Zhihao Li, Shiyong Liu, Xiao Tang, Jiajun Tang, Jiaqi Lin, Yuxin Cheng, Zhenyu Chen, Xiaofei Wu, Ngai Wong
IJCAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 15,256 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,596 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 demonstrate that the hybrid representation maintains comparable rendering quality and achieves superior frames per second FPS with fewer Gaussian primitives. |
| Researcher Affiliation | Collaboration | 1The University of Hong Kong 2Huawei Technologies Ltd 3Peking University 4Tsinghua University EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology in narrative text and figures (e.g., Figure 2 for the overall pipeline) but does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper states, "We build our method upon the open-source 3DGS code." This indicates the use of third-party open-source code but does not explicitly state that the authors' own implementation for the described methodology is publicly released or provide a link to it. |
| Open Datasets | Yes | We verify the effectiveness of our approach using ten real-world indoor scenes from publicly available datasets: four scenes from the Deep blending [Hedman et al., 2018] and six scenes from Scan Net++ [Yeshwanth et al., 2023]. |
| Dataset Splits | No | The paper mentions evaluating on "test views" but does not explicitly provide details about the specific training/test/validation dataset splits used for their experiments, such as percentages, sample counts, or a detailed splitting methodology. |
| Hardware Specification | Yes | All experiments are conducted on a single V100 GPU. |
| Software Dependencies | No | The paper mentions using "open-source 3DGS code," "PGSR [Chen et al., 2024]," and "Nvdiffrast [Laine et al., 2020]" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Following [Kerbl et al., 2023], we train our models for 30K iterations across all scenes and use the same densification, schedule, and hyperparameters. We set λ to zero after densification iteration (i.e 15k) of 3DGS training. |