DART: Dense Articulated Real-Time Tracking

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Abstract

This paper introduces DART, a general framework for tracking articulated objects composed of rigid bodies connected through a kinematic tree.DART covers a broad set of objects encountered in indoor environments, including furniture and tools, and human and robot bodies, hands and manipulators.To achieve efficient and robust tracking, DART extends the signed distance function representation to articulated objects and takes full advantage of highly parallel GPU algorithms for data association and pose optimization.We demonstrate the capabilities of DART on different types of objects that have each required dedicated tracking techniques in the past.The class of objects that can be described as collections of rigid bodies chained together through a kinematic tree is quite broad, including furniture, tools, human bodies, human hands, and robot manipulators.Tracking articulated bodies from a single viewpoint and without instrumenting the object of interest still presents a significant challenge where the single viewpoint and occlusions, including self-occlusion, limit the amount of information available for pose estimation.Noisy sensor data and approximate object models pose additional problems.Finally, the objects being tracked can be highly dynamic and have many degrees of freedom, making real-time tracking difficult.Early articulated model-based tracking techniques relied on tracking 2D features such as image edges on a CPU [8, 4].Recently introduced depth cameras along with highly parallel algorithms optimized for modern GPUs have enabled new algorithms for tracking complex 3D objects in real time.Examples include KinectFusion and related efforts for 3D mapping [23, 16,34], human body pose tracking [29,35, 15], articulated hand tracking [24,19, 26].These approaches were developed for specific application domains and have not been demonstrated or tested on multiple tracking applications.The application-specific nature of these approaches enables their authors to show excellent performance by taking advantage of domain-specific features and constraints, but it also prevents them from serving as general tools for tracking arbitrary articulated objects.Techniques have also been developed to

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