用单目事件相机实现高精度3D网格重建，首次结合距离函数与事件监督。
EventNeuS: 3D Mesh Reconstruction from a Single Event Camera
- 基于事件流自监督学习，融合有符号距离场与密度场建模
- 在标准数据集上相比最优方法降低34%的Chamfer距离和31%的误差
- 适合做事件相机3D重建的科研与工程人员参考
事件相机在多种场景下可作为RGB相机的有力替代。尽管已有研究探索基于事件的新视角合成，但密集3D网格重建仍鲜有涉及，现有事件相机方法在三维重建精度上存在严重局限。为此，本文提出EventNeuS，一种从单目彩色事件流中学习3D表示的自监督神经模型。我们的方法首次将3D有符号距离函数与密度场学习相结合，并引入事件流进行监督。此外，为更好处理视角依赖效应，我们在模型中引入球谐函数编码。实验表明，EventNeuS显著优于现有方法，在平均上比最佳先前方法降低34%的Chamfer距离和31%的均方绝对误差。
原文摘要 · Abstract (English)
Event cameras offer a considerable alternative to RGB cameras in many scenarios. While there are recent works on event-based novel-view synthesis, dense 3D mesh reconstruction remains scarcely explored and existing event-based techniques are severely limited in their 3D reconstruction accuracy. To address this limitation, we present EventNeuS, a self-supervised neural model for learning 3D representations from monocular colour event streams. Our approach, for the first time, combines 3D signed distance function and density field learning with event-based supervision. Furthermore, we introduce spherical harmonics encodings into our model for enhanced handling of view-dependent effects. EventNeuS outperforms existing approaches by a significant margin, achieving 34% lower Chamfer distance and 31% lower mean absolute error on average compared to the best previous method.
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