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SceneScan Pro
Real-time stereo vision implementation on FPGAs with SceneScan Pro -
1D Target Dataset
The dataset used in this paper is a set of images of a 1D target acquired by a stereo vision system with different distortion models. -
Stereo Vision System
The dataset used in this paper is a set of images of a checkerboard pattern acquired by two cameras with different orientations. -
KITTI Benchmark Dataset
The KITTI benchmark dataset is used to evaluate the performance of the proposed method. The dataset contains large-scale outdoor sequences of images captured by a forward-facing... -
Middlebury 2014
The Middlebury 2014 dataset is a benchmark for stereo matching, consisting of 33 pairs of stereo images with sparse depth ground truth. -
Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite
The KITTI Visual Odometry benchmark dataset consists of 22 stereo sequences with ground truth trajectories. -
FlyingThings3D dataset
The FlyingThings3D dataset is a benchmark for stereo matching, consisting of a large collection of images and corresponding disparity maps. -
KITTI 2012 and 2015 datasets
The KITTI 2012 and 2015 datasets are used for stereo matching experiments. -
KITTI Stereo 2012 and 2015
KITTI Stereo 2012 and 2015 dataset. -
Stereo CenterNet
Stereo CenterNet is a 3D object detection method for stereo images, combining deep learning and geometry. -
KITTI 2012
KITTI 2012 is a real-world dataset in the outdoor scenario, and contains 194 training and 195 testing stereo image pairs with the size of 376 × 1240. -
Natural Scenes
Natural scenes dataset for stereo depth estimation -
KITTI Vision Benchmark Suite
The KITTI Vision Benchmark Suite is a dataset used for object detection and tracking in autonomous vehicles. -
Middlebury
The Middlebury dataset is a benchmark for stereo vision and 3D reconstruction. -
FlyingThings3D
Dense pixel matching is required for many computer vision algorithms such as disparity, optical flow or scene flow estimation. Feature Pyramid Networks (FPN) have proven to be a... -
Learning a Multi-View Stereo Machine
The dataset is used for learning a multi-view stereo machine. -
KITTI 2015
The KITTI 2015 dataset is a real-world dataset of street views, containing 200 training stereo image pairs with sparsely labeled disparity from LiDAR data. -
Scene Flow
Stereo matching aims to recover the dense reconstruction of unknown scenes by computing the disparity from rectified stereo images, helping robots intelligently interact with...
