AERGS-SLAM：面向自动曝光的鲁棒双目三维高斯泼溅 SLAM
AERGS-SLAM: Auto-Exposure-Robust Stereo 3D Gaussian Splatting SLAM
把相机自动曝光过程显式建模为一个可学习的曝光网络并与高斯泼溅联合优化，使 SLAM 在曝光剧烈变化的真实场景下仍能稳定定位并渲染出曝光可控的逼真新视角；对做 3DGS-SLAM 或户外/移动端采集的人，这是一个可直接复用的曝光建模与光照鲁棒特征定位方案。
原文依据 we propose a camera exposure network to model the camera exposure process, which we integrate with Gaussian splatting
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论三维高斯泼溅（3DGS）已成为同步定位与建图（SLAM）研究中一种具有变革性的场景表示。然而，现有基于 3DGS 的 SLAM 研究未能准确处理普遍真实场景中相机自动曝光所引起的外观变化，导致定位精度与逼真建图质量下降。为解决该问题，我们提出双目自动曝光鲁棒高斯泼溅 SLAM（AERGS-SLAM），该框架对此类变化具有鲁棒性，并能同时实现可靠的定位与曝光可控的逼真建图。我们的关键贡献有两方面。首先，我们提出一个相机曝光网络来建模相机曝光过程，并将其与高斯泼溅相结合，以实现曝光可控的新视角合成。其次，我们利用一种光照鲁棒的几何特征用于定位与高斯地图初始化，从而提升曝光变化场景下的定位精度。在公开数据集与我们自采集的真实世界数据集上的大量实验表明，AERGS-SLAM 在定位性能与逼真建图质量上均优于基线方法。我们的代码已公开于：https://github.com/zzy-2021/AERGS-SLAM。
英文原文摘要 1265 字符
3D Gaussian splatting (3DGS) has emerged as a revolutionary scene representation in simultaneous localization and mapping (SLAM) research. However, existing research on 3DGS-based SLAM fails to accurately address the appearance variations induced by camera auto-exposure in prevalent real-world scenarios, resulting in reduced localization and photorealistic mapping accuracy. To address this issue, we propose a stereo auto-exposure-robust Gaussian splatting SLAM (AERGS-SLAM), a framework robust to such variations and enables both reliable localization and exposure-controlled photorealistic mapping. Our key contributions are two fold. Firstly, we propose a camera exposure network to model the camera exposure process, which we integrate with Gaussian splatting to achieve exposure-controlled novel view synthesis. Secondly, we exploit an illumination-robust geometric feature for localization and Gaussian map initialization, enhancing localization accuracy under exposure-varying scenarios. Extensive experiments on public datasets and our self-collected real-world dataset demonstrate that AERGS-SLAM outperforms baselines in both localization performance and photorealistic mapping quality. Our code is available at: https://github.com/zzy-2021/AERGS-SLAM.