E2EGS：面向无位姿三维重建的事件到边缘高斯泼溅
E2EGS: Event-to-Edge Gaussian Splatting for Pose-Free 3D Reconstruction
把事件流中的边缘结构作为几何先验，替代对已知位姿或预训练深度模型的依赖，实现纯事件输入下的无位姿高斯泼溅重建与轨迹估计；其基于 patch 局部方差的时空一致性去噪提边思路，可直接迁移到事件相机 SLAM、高速运动或弱光场景的视觉里程计前端。
原文依据 a patch-based temporal coherence analysis that measures local variance to extract edges while robustly suppressing noise
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论神经辐射场（NeRF）与三维高斯泼溅（3DGS）的出现推动了新视角合成（NVS）的发展。然而，这些方法需要高质量的 RGB 输入以及准确的对应位姿，在快速相机运动或不利光照等真实条件下鲁棒性受限。事件相机以高时序分辨率和宽动态范围捕捉每个像素的亮度变化，能够对动态场景进行精确感知，提供了一种有前景的解决方案。然而，现有基于事件的 NVS 方法要么假设位姿已知，要么依赖受限于初始观测的深度估计模型，当相机经过此前未见的区域时无法泛化。本文提出 E2EGS，一个仅依赖事件流的无位姿框架。我们的关键洞见是：边缘信息提供了丰富的结构线索，对准确的轨迹估计和高质量的 NVS 至关重要。为从含噪事件流中提取边缘，我们利用边缘区域与非边缘区域在时空特性上的差异。事件相机的运动会在边缘处产生一致的事件，而非边缘区域则产生稀疏噪声。我们通过基于图像块的时序一致性分析来利用这一特性，该分析度量局部方差以提取边缘并稳健地抑制噪声。提取出的边缘引导结构感知的高斯初始化，并在初始化、跟踪与光束法平差全过程中启用边缘加权损失。在合成与真实数据集上的大量实验表明，E2EGS 取得了更优的重建质量与轨迹精度，为基于事件的三维重建建立了一个完全无位姿的范式。
英文原文摘要 1701 字符
The emergence of neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS) has advanced novel view synthesis (NVS). These methods, however, require high-quality RGB inputs and accurate corresponding poses, limiting robustness under real-world conditions such as fast camera motion or adverse lighting. Event cameras, which capture brightness changes at each pixel with high temporal resolution and wide dynamic range, enable precise sensing of dynamic scenes and offer a promising solution. However, existing event-based NVS methods either assume known poses or rely on depth estimation models that are bounded by their initial observations, failing to generalize as the camera traverses previously unseen regions. We present E2EGS, a pose-free framework operating solely on event streams. Our key insight is that edge information provides rich structural cues essential for accurate trajectory estimation and high-quality NVS. To extract edges from noisy event streams, we exploit the distinct spatio-temporal characteristics of edges and non-edge regions. The event camera's movement induces consistent events along edges, while non-edge regions produce sparse noise. We leverage this through a patch-based temporal coherence analysis that measures local variance to extract edges while robustly suppressing noise. The extracted edges guide structure-aware Gaussian initialization and enable edge-weighted losses throughout initialization, tracking, and bundle adjustment. Extensive experiments on both synthetic and real datasets demonstrate that E2EGS achieves superior reconstruction quality and trajectory accuracy, establishing a fully pose-free paradigm for event-based 3D reconstruction.