EVIMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras We present the first eventbased learning approach for motion segmentation in indoor scenes and the first eventbased dataset xe2x80x93 EVIMOxe2x80x93 which includes accurate pixelwise motion masks, egomotion and ground truth depth. Our approach is based on an efficient implementation of the SfM learning pipeline using a low parameter neural network architecture on event data. In addition to camera egomotion and a dense depth map, the network estimates independently moving object segmentation at the pixellevel and computes perobject 3D translational velocities of moving objects. We also train a shallow network with just 40k parameters, which is able to compute depth and egomotion.Our EVIMO dataset features 32 minutes of indoor recording with up to 3 fast moving objects in the camera field of view. The objects and the camera are tracked using a VICONxc2xae motion capture system. By 3D scanning the room and the objects, ground truth of the depth map and pixelwise object masks are obtained. We then train and evaluate our learning pipeline on EVIMO and demonstrate that it is well suited for scene constrained robotics applications. SUPPLEMENTARY MATERIAL The supplementary video, code, trained models, appendix and a dataset will be made available at http:prg.cs.umd.eduEVIMO.html.

Improving Learningbased Egomotion Estimation with Homomorphismbased Losses and Drift Correction Visual odometry is an essential problem for mobile robots. Traditional methods for solving VO mostly utilize geometric optimization. While capable of achieving high accuracy, these methods require accurate sensor calibration and complicated parameter tuning to work well in practice. With the rise of deep learning, there has been increased interest in the endtoend, learningbased methods for VO, which have the potential to improve robustness. However, learningbased methods for VO so far are less accurate than geometric methods. We argue that one of the main issues is that the current egomotion estimation task is different from other problems where deep learning has been successful such as object detection. We define a novel cost function for learningbased VO considering the mathematical properties of the group homomorphism. In addition to the standard L2 loss, we incorporate losses based on the identity, inverse and closure properties of SE(3) rigid motion. Furthermore, we propose to reduce the VO drift by estimating the drivable regions using semantic segmentation and incorporate this information into a pose graph optimization. Experiments on KITTI datasets show that the novel cost function can improve egomotion estimation compared to the stateoftheart and the drivable regionbased correction further reduces the VO drift.