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 [1].
GigaGS: 3D Gaussian Based Planar Representation for Large-Scene Surface Reconstruction
Authors: Junyi Chen, Weicai Ye, Yifan Wang, Danpeng Chen, Di Huang, Wanli Ouyang, Guofeng Zhang, Yu Qiao, Tong He
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments are conducted on various datasets. The consistent improvement demonstrates the superiority of Giga GS. |
| Researcher Affiliation | Collaboration | Junyi Chen1,2, Weicai Ye2,3*, Yifan Wang1,2, Danpeng Chen3, Di Huang2, Wanli Ouyang2, Guofeng Zhang3, Yu Qiao2, Tong He2* 1Shanghai Jiao Tong University 2Shanghai Artificial Intelligence Laboratory 3State Key Lab of CAD&CG, Zhejiang University |
| Pseudocode | No | The paper describes the method using prose and mathematical equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://open3dvlab.github.io/Giga GS/ |
| Open Datasets | Yes | We employ Giga GS on datasets consisting of real-life aerial large-scale scenes, which encompass the Building and Rubble scenes extracted from Mill-19 (Turki, Ramanan, and Satyanarayanan 2022), along with the Sci-Art, Campus, and Residence scenes sourced from Urbanscene3d (Liu, Xue, and Huang 2021). |
| Dataset Splits | Yes | To maintain consistency, we employ the same dataset partitioning as Mega Ne RF (Turki, Ramanan, and Satyanarayanan 2022). |
| Hardware Specification | No | The paper mentions distributing the training workload across "multiple GPUs" but does not specify any particular GPU models, CPU details, or other hardware specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | In our experiment, we reduced the side length of 4K aerial images to one-fourth of their original size and aligned them with a comparative method. Subsequently, we employed pixel-sfm (Lindenberger et al. 2021) to obtain an initial point cloud from the aerial images and performed Manhattan world alignment, aligning the y-axis perpendicular to the world coordinate axis of the ground plane. We divided the entire scene into 4 2 partitions in the case of rubble, building, residence, and sci-art, while for the largest scene, campus, we divided it into 4 4 partitions. Each partition was subjected to training for 120, 000 iterations to ensure sufficient convergence. |