GSDet: Gaussian Splatting for Oriented Object Detection
Authors: Zeyu Ding, Jiaqi Zhao, Yong Zhou, Wen-liang Du, Hancheng Zhu, Rui Yao
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 3 datasets indicate that GSDet achieves AP50 gains of 0.7% on DIOR-R, 0.3% on DOTA-v1.0, and 0.55% on DOTA-v1.5 when evaluated with adaptive control and outperforms mainstream detectors. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, China University of Mining and Techology 2Mine Digitization Engineering Research Center of the Ministry of Education EMAIL |
| Pseudocode | Yes | Algorithm 1 GSDet Training |
| Open Source Code | Yes | Code link https://github.com/wokaikaixinxin/GSDet. |
| Open Datasets | Yes | We conduct extensive experiments on three datasets DOTA-v1.0 [Xia et al., 2018], DOTA-v1.5 [Xia et al., 2018] and DIOR-R [Cheng et al., 2022a]. |
| Dataset Splits | Yes | DOTA-v1.0 [Xia et al., 2018] comprises 1,869 images in the trainval set and 937 images in the test set, annotated with 188,282 instances across 15 categories. DOTA-v1.5 [Xia et al., 2018] dataset extends the DOTA-v1.0 dataset by adding a new category named Container Crane while keeping the same images. The number of instances is increased to 403,318 in total. DIOR-R [Cheng et al., 2022a] dataset consists of 11,725 training images in the trainval set, 11,738 test images in the test set and 192,512 instances belonging to 20 categories. |
| Hardware Specification | Yes | All models are trained with the batchsize 4 on two Nvidia 2080ti (2 images per GPU). |
| Software Dependencies | No | Our code is built on MMrotate with pytorch. No specific version numbers for MMrotate or Pytorch are provided, which prevents full reproducibility. |
| Experiment Setup | Yes | The optimizer Adam W [Loshchilov and Hutter, 2018] is used with the learning rate as 2.5 10 5 and the weight decay as 10 4. All models are trained with the batchsize 4 on two Nvidia 2080ti (2 images per GPU). The training schedule is 24 epochs, with the learning rate divided by 16 and 22 epochs. Data augmentation strategies contain only random flips. |