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Found 7 papers, 1 papers with code
SceneCalib: Automatic Targetless Calibration of Cameras and Lidars in Autonomous Driving
Accurate camera-to-lidar calibration is a requirement for sensor data fusion in many 3D perception tasks.
EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms
Depth and segmentation are provided at 60 Hz for the event cameras and 30 Hz for the classical camera.
Learning Visual Motion Segmentation Using Event Surfaces
In this work we present a Graph Convolutional neural network for the task of scene motion segmentation by a moving camera.
Network Deconvolution
Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image.
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras
In addition to camera egomotion and a dense depth map, the network estimates pixel-wise independently moving object segmentation and computes per-object 3D translational velocities for moving objects.
Unsupervised Learning of Dense Optical Flow, Depth and Egomotion from Sparse Event Data
In this work we present a lightweight, unsupervised learning pipeline for \textit{dense} depth, optical flow and egomotion estimation from sparse event output of the Dynamic Vision Sensor (DVS).
Event-based Moving Object Detection and Tracking
Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are ideally suited for real-time motion analysis.
