标题： 光子喷溅：从SPAD传感器进行3D场景重建和着色 显示英文标题

标题： PhotonSplat: 3D Scene Reconstruction and Colorization from SPAD Sensors

摘要： 使用神经渲染进行3D重建的进展已经实现了高质量的3D捕获。 然而，当输入图像受到运动模糊的损坏时，它们通常会失败，这是由于相机或场景中物体的快速运动造成的。 这项工作通过使用单光子雪崩二极管（SPAD）阵列来推进此类情况下的神经渲染技术，这是一种能够以极高速度感测图像的新兴传感技术。 然而，使用SPADs会以二进制图像的形式带来其自身的一系列独特挑战，这些二进制图像由随机光子到达驱动。 为了解决这个问题，我们引入了PhotonSplat，一个直接从SPAD二进制图像重建3D场景的框架，有效地处理噪声与模糊之间的权衡。 我们的方法结合了一种新颖的3D空间过滤技术来减少渲染中的噪声。 该框架还支持无需参考的基于生成先验的颜色化和从单个模糊图像进行参考的颜色化，使下游应用如分割、目标检测和外观编辑任务成为可能。 此外，我们将方法扩展到包含动态场景表示，使其适用于有移动物体的场景。 我们进一步贡献了PhotonScenes，一个使用SPAD传感器拍摄的真实世界多视角数据集。 显示英文摘要

摘要： Advances in 3D reconstruction using neural rendering have enabled high-quality 3D capture. However, they often fail when the input imagery is corrupted by motion blur, due to fast motion of the camera or the objects in the scene. This work advances neural rendering techniques in such scenarios by using single-photon avalanche diode (SPAD) arrays, an emerging sensing technology capable of sensing images at extremely high speeds. However, the use of SPADs presents its own set of unique challenges in the form of binary images, that are driven by stochastic photon arrivals. To address this, we introduce PhotonSplat, a framework designed to reconstruct 3D scenes directly from SPAD binary images, effectively navigating the noise vs. blur trade-off. Our approach incorporates a novel 3D spatial filtering technique to reduce noise in the renderings. The framework also supports both no-reference using generative priors and reference-based colorization from a single blurry image, enabling downstream applications such as segmentation, object detection and appearance editing tasks. Additionally, we extend our method to incorporate dynamic scene representations, making it suitable for scenes with moving objects. We further contribute PhotonScenes, a real-world multi-view dataset captured with the SPAD sensors.