Segment Any 3D Gaussians
Authors: Jiazhong Cen, Jiemin Fang, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations demonstrate that SAGA achieves real-time multi-granularity segmentation with quality comparable to state-of-the-art methods. As one of the first methods addressing promptable segmentation in 3D-GS, the simplicity and effectiveness of SAGA pave the way for future advancements in this field. |
| Researcher Affiliation | Collaboration | 1Mo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University 2Huawei Technologies Co., Ltd. EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology and training strategy in sections 3, 3.1, 3.2, 3.3, 3.4, and 3.5 using prose and mathematical equations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/Jumpat/Seg Any GAussians |
| Open Datasets | Yes | For promptable segmentation experiments, we utilize two datasets: NVOS (Ren et al. 2022) and SPIn-Ne RF(Mirzaei et al. 2023). ... For open-vocabulary segmentation experiments, we adopt the 3D-OVS dataset (Liu et al. 2023a). For qualitative analysis (Figure 3 and 4), we employ various datasets including LLFF (Mildenhall et al. 2019), MIP-360 (Barron et al. 2022), Tanks&Temple (Knapitsch et al. 2017), and Replica (Straub et al. 2019). |
| Dataset Splits | No | The paper uses several datasets for experiments including NVOS, SPIn-Ne RF, 3D-OVS, LLFF, MIP-360, Tanks&Temple, and Replica, but does not provide specific details on how these datasets were split into training, validation, or test sets. It refers to Section A.2 for implementation details and experiment settings, which is not available in the provided text. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory specifications used for running the experiments. It refers to Section A.2 for implementation details and experiment settings, which is not available in the provided text. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, used to replicate the experiment. It refers to Section A.2 for implementation details and experiment settings, which is not available in the provided text. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. It refers to Section A.2 for implementation details and experiment settings, which is not available in the provided text. |