Temporal Up-Sampling for Asynchronous Events

Abstract

The event camera is a novel bio-inspired vision sensor. When the brightness change exceeds the preset threshold, the sensor generates events asynchronously. The number of valid events directly affects the performance of event-based tasks, such as reconstruction, detection, and recognition. However, when in low-brightness or slow-moving scenes, events are often sparse and accompanied by noise, which poses challenges for event-based tasks. To solve these challenges, we propose an event temporal up-sampling algorithm 1 to generate more effective and reliable events. The main idea of our algorithm is to generate up-sampling events on the event motion trajectory. First, we estimate the event motion trajectory by contrast maximization algorithm and then up-sampling the events by temporal point processes. Experimental results show that upsampling events can provide more effective information and improve the performance of downstream tasks, such as improving the quality of reconstructed images and increasing the accuracy of object detection.

Secrets of Event-Based Optical Flow, Depth and Ego-Motion Estimation by Contrast Maximization

“…By extending the idea of [84], which classifies events for temporal upsampling into signal or noise based on a predicted 2-DOF motion, we use the estimated optical flow to identify noise events as those where the IWE is smaller than some value (e.g., 3 events). Figure 13 shows qualitative results.…”