Monocular Depth Estimation (MDE) is a fundamental problem in computer vision with numerous applications. Recently, LIDAR-supervised methods have achieved re-markable per-pixel depth accuracy in outdoor scenes. However, significant errors are typically found in the proximity of depth discontinuities, i.e., depth edges, which often hin-der the performance of depth-dependent applications that are sensitive to such inaccuracies, e.g., novel view synthe-sis and augmented reality. Since direct supervision for the location of depth edges is typically unavailable in sparse LIDAR-based scenes, encouraging the MDE model to produce correct depth edges is not straightforward. To the best of our knowledge this paper is the first attempt to address the depth edges issue for LIDAR-supervised scenes. In this work we propose to learn to detect the location of depth edges from densely-supervised synthetic data, and use it to generate supervision for the depth edges in the MDE training. To quantitatively evaluate our approach, and due to the lack of depth edges GT in LIDAR-based scenes, we manually annotated subsets of the KITTI and the DDAD datasets with depth edges ground truth. We demonstrate significant gains in the accuracy of the depth edges with comparable per-pixel depth accuracy on several challenging datasets. Code and datasets are available at htt ps: //github.com/liortalker/MindTheEdge.

Detecting an object part relies on two sources of information - the appearance of the part itself and the context supplied by surrounding parts. In this paper we consider problems in which a target part cannot be recognized reliably using its own appearance, such as detecting low-resolution hands, and must be recognized using the context of surrounding parts. We develop the `chains model' which can locate parts of interest in a robust and precise manner, even when the surrounding context is highly variable and deformable. In the proposed model, the relation between context features and the target part is modeled in a non-parametric manner using an ensemble of feature chains leading from parts in the context to the detection target. The method uses the configuration of the features in the image directly rather than through fitting an articulated 3-D model of the object. In addition, the chains are composable, meaning that new chains observed in the test image can be composed of sub-chains seen during training. Consequently, the model is capable of handling object poses which are infrequent, even non-existent, during training. We test the approach in different settings, including object parts detection, as well as complete object detection. The results show the advantages of the chains model for detecting and localizing parts of complex deformable objects.

This paper presents an approach to the visual recognition of human actions using only single images as input. The task is easy for humans but difficult for current approaches to object recognition, because action instances may be similar in terms of body pose, and often require detailed examination of relations between participating objects and body parts in order to be recognized. The proposed approach applies a two-stage interpretation procedure to each training and test image. The first stage produces accurate detection of the relevant body parts of the actor, forming a prior for the local evidence needed to be considered for identifying the action. The second stage extracts features that are ‘anchored’ to the detected body parts, and uses these features and their feature-to-part relations in order to recognize the action. The body anchored priors we propose apply to a large range of human actions. These priors allow focusing on the relevant regions and relations, thereby significantly simplifying the learning process and increasing recognition performance.

Class learning, both supervised and unsupervised, requires feature selection, which includes two main components. The first is the selection of a discriminative subset of features from a larger pool. The second is the selection of detection parameters for each feature to optimize classification performance. In this paper we present a method for the discovery of multiple classification features, their detection parameters and their consistent configurations, in the fully unsupervised setting. This is achieved by a global optimization of joint consistency between the features as a function of the detection parameters, without assuming any prior parametric model. We demonstrate how the proposed framework can be applied for learning different types of feature parameters, such as detection thresholds and geometric relations, resulting in the unsupervised discovery of informative configurations of objects parts. We test our approach on a wide range of classes and show good results. We also demonstrate how the approach can be used to unsupervisedly separate and learn visually similar sub-classes of a single category, such as facial views or hand poses. We use the approach to compare various criteria for feature consistency, including Mutual Information, Suspicious Coincidence, L2 and Jaccard index. Finally, we compare our approach to aparametric consistency optimization technique such as pLSA and show significantly better performance.