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The problem of graph matching under node and pairwise constraints is fundamental in areas as diverse as combinatorial optimization, machine learning or computer vision, where representing both the relations between nodes and their neighborhood structure is essential. We present an end-to-end model that makes it possible to learn all parameters of the graph matching process, including the unary and
We propose drl-RPN, a deep reinforcement learning-based visual recognition model consisting of a sequential region proposal network (RPN) and an object detector. In contrast to typical RPNs, where candidate object regions (RoIs) are selected greedily via class-agnostic NMS, drl-RPN optimizes an objective closer to the final detection task. This is achieved by replacing the greedy RoI selection pro
Most 3d human pose estimation methods assume that input – be it images of a scene collected from one or several viewpoints, or from a video – is given. Consequently, they focus on estimates leveraging prior knowledge and measurement by fusing information spatially and/or temporally, whenever available. In this paper we address the problem of an active observer with freedom to move and explore the
We study the task of embodied visual active learning, where an agent is set to explore a 3d environment with the goal to acquire visual scene understanding by actively selecting views for which to request annotation. While accurate on some benchmarks, today's deep visual recognition pipelines tend to not generalize well in certain real-world scenarios, or for unusual viewpoints. Robotic perception
To estimate the 6-DoF extrinsic pose of a pinhole camera with partially unknown intrinsic parameters is a critical sub-problem in structure-from-motion and camera localization. In most of existing camera pose estimation solvers, the principal point is assumed to be in the image center. Unfortunately, this assumption is not always true, especially for asymmetrically cropped images. In this paper, w
This paper presents a discrete model of a sensor path planning problem, with a long-term planning horizon. The goal is to minimize the covariance of the reconstructed structures while meeting constraints on the length of the traversed path of the sensor. The sensor is restricted to move on a graph representing a discrete set of configurations, and additional constraints can be incorporated by alte
Measurements with unknown time delays are common in different applications such as microphone array, radio antenna array calibration, where the sources (e.g. sounds) are transmitted in unknown time instants. In this paper, we present a method for estimating unknown time delays from Time-Difference-of-Arrival (TDOA) measurements. We propose a novel rank constraint on a matrix depending on the measu
The durability of a road is among other factors dependent on the affinity between stones in the top layer and bitumen that holds the stones together. Poor adherence will cause stones to detach from the surface of the road more easily. The rolling bottle method is the standard way to determine the affinity between stones and bitumen. In this test a number of stones covered in bitumen are put in a r
This paper introduces the first minimal solvers that jointly solve for affine-rectification and radial lens distortion from coplanar repeated patterns. Even with imagery from moderately distorted lenses, plane rectification using the pinhole camera model is inaccurate or invalid. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle
We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism. The tasks present several challenges: a large dataset with long videos, a large number of highly variable actions, children that are only partially visible, have different ages and may show unpredictable behaviour, as well as non
In this paper, we study the minimal problem of estimating the essential matrix between two cameras with constant but unknown focal length and radial distortion. This problem is of both theoretical and practical interest and it has not been solved previously. We have derived a fast and stable polynomial solver based on Gr{\"o}bner basis method. This solver enables simultaneous auto-calibration of f
In this paper we consider the epipolar geometry between orthographic and perspective cameras. We generalize many of the classical results for the perspective essential matrix to this setting and derive novel minimal solvers, not only for the calibrated case, but also for partially calibrated and non-central camera setups. While orthographic cameras might seem exotic, they occur naturally in many a
In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circu
Indoor localization and navigation is a much researched and difficult problem. The best solutions, usually use expensive specialized equipment and/or prior calibration of some form. To the average person with smart or Internet-Of-Things devices, these solutions are not feasible, particularly in large scales. With hardware advancements making Ultra-Wideband devices more accurate and low powered, th
Understanding 3d human interactions is fundamental for fine grained scene analysis and behavioural modeling. However, most of the existing models focus on analyzing a single person in isolation, and those who process several people focus largely on resolving multi-person data association, rather than inferring interactions. This may lead to incorrect, lifeless 3d estimates, that miss the subtle hu
This paper describes a multi-camera system designed for capture and tracking of swimmers both above and below the surface of a pool. To be able to measure the swimmer's position, the cameras need to be accurately calibrated. Images captured below the surface provide a number of challenges, mainly due to refraction and reflection effects at optical media boundaries. We present practical methods for
Many computer vision applications require robust estimation of the underlying geometry, in terms of camera motion and 3D structure of the scene. These robust methods often rely on running minimal solvers in a RANSAC framework. In this paper we show how we can make polynomial solvers based on the action matrix method faster, by careful selection of the monomial bases. These monomial bases have trad
The aim of this workshop was to discuss the needs and challenges in using high-resolution climate model outputs for impacts-relevant modeling. Development of impacts-relevant climate projections in the polar regions requires effective collaboration between regional climate modelers and impacts-relevant modelers in the design stage of high-resolution climate projections for the polar regions.
Estimating the 3D model of the human body is needed for many applications. However, this is a challenging problem since the human body inherently has a high complexity due to self-occlusions and articulation. We present a method to reconstruct the 3D human body model from a single RGB-D image. 2D joint points are firstly predicted by a CNN-based model called convolutional pose machine, and the 3D
