Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning method, and validate our approach by building and publicly releasing a new video dataset, called Virtual KITTI (see this http URL), automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow. We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance. As the gap between real and virtual worlds is small, virtual worlds enable measuring the impact of various weather and imaging conditions on recognition performance, all other things being equal. We show these factors may affect drastically otherwise high-performing deep models for tracking.
Only few works use photo-realistic imagery for , and in most cases these works focus on low-level image and video processing tasks. Kaneva al @cite_39 evaluate low-level image features, while Butler al @cite_26 propose a synthetic benchmark for optical flow estimation: the popular MPI Sintel Flow Dataset. The recent work of Chen al @cite_10 is another example for basic building blocks of autonomous driving. These approaches view photo-realistic imagery as a way of obtaining ground truth that cannot be easily obtained otherwise ( optical flow). When ground-truth can be collected, for instance via crowd-sourcing, real-world imagery is often preferred over synthetic data because of the artifacts the latter might introduce.
@cite_26 @cite_10 @cite_39
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We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving. We build on the observation that the scene is typically composed of a static background, as well as a relatively small number of traffic participants which move rigidly in 3D. We propose to estimate the traffic participants using instance-level segmentation. For each traffic participant, we use the epipolar constraints that govern each independent motion for faster and more accurate estimation. Our second contribution is a new convolutional net that learns to perform flow matching, and is able to estimate the uncertainty of its matches. This is a core element of our flow estimation pipeline. We demonstrate the effectiveness of our approach in the challenging KITTI 2015 flow benchmark, and show that our approach outperforms published approaches by a large margin. We propose to look at large-displacement optical flow from a discrete point of view. Motivated by the observation that sub-pixel accuracy is easily obtained given pixel-accurate optical flow, we conjecture that computing the integral part is the hardest piece of the problem. Consequently, we formulate optical flow estimation as a discrete inference problem in a conditional random field, followed by sub-pixel refinement. Naive discretization of the 2D flow space, however, is intractable due to the resulting size of the label set. In this paper, we therefore investigate three different strategies, each able to reduce computation and memory demands by several orders of magnitude. Their combination allows us to estimate large-displacement optical flow both accurately and efficiently and demonstrates the potential of discrete optimization for optical flow. We obtain state-of-the-art performance on MPI Sintel and KITTI. Optical flow research has made significant progress in recent years and it can now be computed efficiently and accurately for many images. However, complex motions, large displacements, and difficult imaging conditions are still problematic. In this paper, we present a framework for estimating optical flow which leads to improvements on these difficult cases by 1) estimating occlusions and 2) using additional temporal information. First, we divide the image into discrete triangles and show how this allows for occluded regions to be naturally estimated and directly incorporated into the optimization algorithm. We additionally propose a novel method of dealing with temporal information in image sequences by using “inertial estimates” of the flow. These estimates are combined using a classifier-based fusion scheme, which significantly improves results. These contributions are evaluated on three different optical flow datasets, and we achieve state-of-the-art results on MPI-Sintel. We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90 of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic.