This paper tackles the speed-resolution trade-off using event cameras. Event cameras are efficient highspeed vision sensors that asynchronously measure changes in brightness intensity with microsecond resolution.
This paper showcases the viability of integrating conventional algorithms with event-based data, transformed into a frame format while preserving the unique benefits of event cameras.
This paper proposes a novel, computationally efficient regularizer to mitigate event collapse in the CMax framework. From a theoretical point of view, the regularizer is designed based on geometric principles of motion field deformation (measuring area rate of change along point trajectories).
This paper compares two methods of processing event data by means of deep learning for the task of pedestrian detection. It uses a representation in the form of video frames, convolutional neural networks and asynchronous sparse convolutional neural networks.
This paper introduces a novel minimal 5-point solver that jointly estimates line parameters and linear camera velocity projections, which can be fused into a single, averaged linear velocity when considering multiple lines.