用事件相机扫焦生成稠密深度图，性能优于传统方法。
Dense Depth from Event Focal Stack
- 通过事件焦点堆栈与卷积网络联合建模深度
- 在合成与真实数据上均优于图像域去模糊深度方法
- 自研合成数据增强真实感，有效缩小域差距
我们提出一种从事件相机在驱动镜头扫焦过程中产生的事件流中估计稠密深度的方法。该方法利用由卷积神经网络训练的“事件焦点堆栈”来推断深度图，其中合成事件流基于Blender为任意3D场景生成的焦点堆栈构建，支持多样结构场景的训练。此外，我们探索了消除真实事件流与合成事件流之间域差异的方法。实验表明，该方法在合成与真实数据集上的表现均优于图像域的深度-失焦方法。
原文摘要 · Abstract (English)
We propose a method for dense depth estimation from an event stream generated when sweeping the focal plane of the driving lens attached to an event camera. In this method, a depth map is inferred from an ``event focal stack'' composed of the event stream using a convolutional neural network trained with synthesized event focal stacks. The synthesized event stream is created from a focal stack generated by Blender for any arbitrary 3D scene. This allows for training on scenes with diverse structures. Additionally, we explored methods to eliminate the domain gap between real event streams and synthetic event streams. Our method demonstrates superior performance over a depth-from-defocus method in the image domain on synthetic and real datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。