Stereo Depth Estimation

KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odometry challenge) with 11 classes: building, tree, sky, car, sign, road, pedestrian, fence, pole, sidewalk, and bicyclist. Source: A Review on Deep Learning Techniques Applied to Semantic Segmentation Image Source: http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d

Spring is a large, high-resolution and high-detail, computer-generated benchmark for scene flow, optical flow, and stereo. Based on rendered scenes from the open-source Blender movie "Spring", it provides photo-realistic HD datasets with state-of-the-art visual effects and ground truth training data. Source: Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo Image Source: https://arxiv.org/pdf/2303.01943v1.pdf

The Helvipad dataset is a real-world stereo dataset designed for omnidirectional depth estimation. It comprises 39,553 paired equirectangular images captured using a top-bottom 360° camera setup and corresponding pixel-wise depth and disparity labels derived from LiDAR point clouds. The dataset spans diverse indoor and outdoor scenes under varying lighting conditions, including night-time environments.

This dataset presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing datasets by simultaneously addressing several issues, such as environment diversity, motion patterns, and sensor frequency. It uses up-to-date devices and presents effective procedures to accurately calibrate the intrinsic and extrinsic of the sensors while addressing temporal synchronization. During recording, we cover multi-floor buildings, gardens, urban and highway scenarios. Combining handheld and car-based data collections, our setup can simulate any robot (quadrupeds, quadrotors, autonomous vehicles). The dataset includes an accurate 6-dof ground truth based on a novel methodology that refines the RTK-GPS estimate with LiDAR point clouds through Bundle Adjustment. All sequences divided in training and testing are accessible through our website.

We provide all the expected data inputs to GUISS such as meshes, texture images, and blend files. Generated datasets used in our experiments along with the stereo depth estimations can be downloaded. We have defined seven dataset types: scene_reconstructions, texture_variation, gaea_texture_variation, generative_texture, terrain_variation, rocks, and generative_texture_snow. Each dataset type contains renderings with varying values of different parameters such as lighting angle, texture imgs, albedo, etc. Position each dataset type folder under data/dataset/. Details and links at: https://github.com/nasa-jpl/guiss

Abstract: We introduce the multi-spectral stereo (MS2) outdoor dataset, including stereo RGB, stereo NIR, stereo thermal, stereo LiDAR data, and GPS/IMU information. Our dataset provides rectified and synchronized 184K data pairs taken from city, residential, road, campus, and suburban areas in the morning, daytime, and nighttime under clear-sky, cloudy, and rainy conditions. We designed the dataset to explore various computer vision algorithms from multi-spectral sensor data to achieve high-level performance, reliability, and robustness against challenging environments. MS2 dataset provides: * 1. (Synchronized) Stereo RGB images / Stereo NIR images / Stereo thermal images * 2. (Synchronized) Stereo LiDAR scans / GPS/IMU navigation data * 3. Projected depth map (in RGB, NIR, thermal image planes) * 4. Odometry data (in RGB, NIR, thermal cameras, and LiDAR coordinates)