E2SL: Efficient Depth Sensing from Event-Based Structured Light

Structured light (SL) is a popular approach for 3D reconstruction. Most SL techniques rely on frame-based cameras and are often not robust in high-speed dynamic scenes. Recently, event cameras have sparked growing interest in high-speed SL imaging, due to their high temporal resolution. The event-based SL enjoys the high-speed data acquisition, however, most existing methods tend to pursue the reconstruction accuracy but sacrificing the computational efficiency, limiting the applicability in real-world scenarios. To this end, we propose E2SL, an Efficient deep network tailored for monocular Event-based SL. Specifically, E2SL comprises three key components: binary embedding lookup table (BE-LUT), spatial context enhancement (SCE), and geometric-prior regression (GPR). Given the input event frame, BE-LUT, which is precomputed and stored, first retrieves the features efficiently. Then, SCE extends the receptive field of the features and captures the spatial context. Finally, GPR conducts the geometric-prior-based tree classification for fast and robust depth estimation. To support training and evaluation, we contribute an event-based SL simulator, which generates a large-scale and diverse synthetic dataset. Besides, we develop an event-based SL prototype and collect a dataset with accurate ground truth for real-world evaluation. Extensive experiments demonstrate that our method achieves state-of-the-art accuracy while maintaining a per-frame reconstruction time of 7.7 ms, meeting the demands of high-speed depth sensing. The code and dataset are available on the project page https://dongxin000.github.io/E2SL/.