arXiv 2603.146842026-03-16cs.CV

E2EGS：基于事件的边缘高斯点云化用于无姿态3D重建

E2EGS: Event-to-Edge Gaussian Splatting for Pose-Free 3D Reconstruction

Yunsoo Kim, Changki Sung, Dasol Hong, Hyun Myung

备注：: 10 pages, 6 figures, accepted to CVPR 2026

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AI总结：

E2EGS通过利用事件流中的边缘信息，提出无姿态3D重建框架，提升动态场景下的重建质量和轨迹精度。

摘要：

神经辐射场（NeRF）和3D高斯点云化（3DGS）的出现推动了新型视图合成（NVS）的发展。然而，这些方法需要高质量的RGB输入和准确的对应姿态，限制了在现实条件下的鲁棒性，如快速相机运动或恶劣光照。事件相机能够以高时间分辨率和宽动态范围捕捉每个像素的亮度变化，可提供动态场景的精确感知，并提供有前景的解决方案。然而，现有的基于事件的NVS方法要么假设已知姿态，要么依赖于受初始观察限制的深度估计模型，无法在相机遍历未见过的区域时泛化。我们提出了E2EGS，一个仅基于事件流的无姿态框架。我们的关键见解是边缘信息提供了丰富的结构线索，对于准确轨迹估计和高质量NVS至关重要。为了从嘈杂的事件流中提取边缘，我们利用边缘和非边缘区域的时空特征差异。事件相机的运动会在边缘上产生一致的事件，而非边缘区域会产生稀疏噪声。我们通过基于补丁的时序一致性分析来利用这一点，通过测量局部方差来提取边缘并稳健地抑制噪声。提取的边缘指导结构感知的高斯初始化，并在整个初始化、跟踪和束调整过程中启用边缘加权损失。在合成和真实数据集上的大量实验表明，E2EGS在重建质量和轨迹精度上均优于现有方法，建立了基于事件的3D重建的无姿态范式。

英文摘要：

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.

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