We present the first events-only static-obstacle avoidance method for a quadrotor with just an onboard, monocular event camera. By leveraging depth prediction as an intermediate step in our learning framework, we can pre-train a reactive obstacle avoidance events-to-control policy in simulation, and then fine-tune the perception component with limited events-depth real-world data to achieve dodging in indoor and outdoor settings.
In this work, we propose a dual-camera system consisting of an event camera and a conventional RGB camera for video motion magnification, providing temporally-dense information from the event stream and spatially-dense data from the RGB images. This innovative combination enables a broad and cost-effective amplification of high-frequency motions.
Our previous work demonstrated the early development of neuromorphic imaging cytometry, evaluating its feasibility in resolving conventional frame-based imaging systems’ limitations in data redundancy, fluorescence sensitivity, and compromised throughput. Herein, we adopted a convolutional spiking neural network (SNN) combined with the YOLOv3 model (SNN-YOLO) to perform cell classification and detection on label-free samples under neuromorphic vision.
We introduce a method to detect full RGB events using a monochrome EC aided by a structured light projector. We combine the benefits of ECs and projection-based techniques and allow depth and color detection of static or moving objects with a commercial TI LightCrafter 4500 projector and a monocular monochrome EC, paving the way for frameless RGB-D sensing applications.
We propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation. Our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods. We develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features.
