DISC: A Largescale Virtual Dataset for Simulating Disaster Scenarios In this paper, we present the first largescale synthetic dataset for visual perception in disaster scenarios, and analyze stateoftheart methods for multiple computer vision tasks with reference baselines. We simulated before and after disaster scenarios such as fire and building collapse for fifteen different locations in realistic virtual worlds. The dataset consists of more than 300K highresolution stereo image pairs, all annotated with groundtruth data for semantic segmentation, depth, optical flow, surface normal estimation and camera pose estimation. To create realistic disaster scenes, we manually augmented the effects with 3D models using physicalbased graphics tools. We use our dataset to train stateoftheart methods and evaluate how well these methods can recognize the disaster situations and produce reliable results on virtual scenes as well as realworld images. The results obtained from each task are then used as inputs to the proposed visual odometry network for generating 3D maps of buildings on fire. Finally, we discuss challenges for future research.

DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation We present a novel dataset for training and benchmarking semantic SLAM methods. The dataset consists of 200 long sequences, each one containing 30005000 data frames. We generate the sequences using realistic home layouts. For that we sample trajectories that simulate motions of a simple home robot, and then render the frames along the trajectories. Each data frame contains a) RGB images generated using physicallybased rendering, b) simulated depth measurements, c) simulated IMU readings and d) ground truth occupancy grid of a house. Our dataset serves a wider range of purposes compared to existing datasets and is the first largescale benchmark focused on the mapping component of SLAM. The dataset is split into trainvalidationtest parts sampled from different sets of virtual houses. We present benchmarking results for both classical geometrybased 1, 2 and recent learningbased 3 SLAM algorithms, a baseline mapping method 4, semantic segmentation 5 and panoptic segmentation 6. The dataset and source code for reproducing our experiments will be publicly available at the time of publication.