Papers › Discrete Time Convolution for Fast Event-Based Stereo
Discrete Time Convolution for Fast Event-Based Stereo
Kaixuan Zhang, Kaiwei Che, JianGuo Zhang, Jie Cheng, Ziyang Zhang, Qinghai Guo, Luziwei Leng
Inspired by biological retina, dynamical vision sensor transmits events of instantaneous changes of pixel intensity, giving it a series of advantages over traditional frame-based camera, such as high dynamical range, high temporal resolution and low power consumption. However, extracting information from highly asynchronous event data is a challenging task. Inspired by continuous dynamics of biological neuron models, we propose a novel encoding method for sparse events - continuous time convolution (CTC) - which learns to model the spatial feature of the data with intrinsic dynamics. Adopting channel-wise parameterization, temporal dynamics of the model is synchronized on the same feature map and diverges across different ones, enabling it to embed data in a variety of temporal scales. Abstracted from CTC, we further develop discrete time convolution (DTC) which accelerates the process with lower computational cost. We apply these methods to event-based multi-view stereo matching where they surpass state-of-the-art methods on benchmark criteria of the MVSEC dataset. Spatially sparse event data often leads to inaccurate estimation of edges and local contours. To address this problem, we propose a dual-path architecture in which the feature map is complemented by underlying edge information from original events extracted with spatially-adaptive denormalization. We demonstrate the superiority of our model in terms of speed (up to 110 FPS), accuracy and robustness, showing a great potential for real-time fast depth estimation. Finally, we perform experiments on the recent DSEC dataset to demonstrate the general usage of our model.
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