无需额外标注，用3D高斯点云重建驾驶场景并自动打标签。
EGSRAL: An Enhanced 3D Gaussian Splatting based Renderer with Automated Labeling for Large-Scale Driving Scene
- 仅用图像训练，不依赖深度图、3D框等额外标注。
- nuScenes数据集上PSNR达29.04，优于现有方法。
- 自动标注提升2D/3D检测性能，适合自动驾驶场景重建。
3D高斯点云（3D GS）因其快速渲染和高质量新视角合成而受到关注。已有研究尝试用3D GS重建驾驶场景，但通常依赖深度图、3D边界框及运动物体轨迹等多类型数据，且合成图像缺乏标注，限制了其在下游任务中的直接应用。为此，我们提出EGSRAL，一种仅依赖训练图像的3D GS方法，可同时建模动态物体与静态背景，并引入新颖的自适应器实现自动标注，基于已有标注生成对应标签。此外，我们设计了一种分组策略，解决大规模复杂场景中视角失真问题。该方法在多个数据集上达到当前最优性能，例如在nuScenes数据集上PSNR达29.04，且无需任何额外标注。自动标注还能显著提升2D/3D目标检测任务表现。代码已开源：https://github.com/jiangxb98/EGSRAL。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3D GS) has gained popularity due to its faster rendering speed and high-quality novel view synthesis. Some researchers have explored using 3D GS for reconstructing driving scenes. However, these methods often rely on various data types, such as depth maps, 3D boxes, and trajectories of moving objects. Additionally, the lack of annotations for synthesized images limits their direct application in downstream tasks. To address these issues, we propose EGSRAL, a 3D GS-based method that relies solely on training images without extra annotations. EGSRAL enhances 3D GS's capability to model both dynamic objects and static backgrounds and introduces a novel adaptor for auto labeling, generating corresponding annotations based on existing annotations. We also propose a grouping strategy for vanilla 3D GS to address perspective issues in rendering large-scale, complex scenes. Our method achieves state-of-the-art performance on multiple datasets without any extra annotation. For example, the PSNR metric reaches 29.04 on the nuScenes dataset. Moreover, our automated labeling can significantly improve the performance of 2D/3D detection tasks. Code is available at https://github.com/jiangxb98/EGSRAL.
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