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A State of the Art Report on Kinect Sensor Setups in Computer Vision
"... Abstract. During the last three years after the launch of the Microsoft Kinect R ○ in the end-consumer market we have become witnesses of a small revolution in computer vision research towards the use of a standardized consumer-grade RGBD sensor for scene content retrieval. Beside classical localiza ..."
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Abstract. During the last three years after the launch of the Microsoft Kinect R ○ in the end-consumer market we have become witnesses of a small revolution in computer vision research towards the use of a standardized consumer-grade RGBD sensor for scene content retrieval. Beside classical localization and motion capturing tasks the Kinect has successfully been employed for the reconstruction of opaque and transparent objects. This report gives a comprehensive overview over the main publications using the Microsoft Kinect out of its original context as a decision-forest based motion-capturing tool. 1
DART: Dense Articulated Real-Time Tracking
"... Abstract—This paper introduces DART, a general framework for tracking articulated objects composed of rigid bodies con-nected 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 manipula ..."
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Abstract—This paper introduces DART, a general framework for tracking articulated objects composed of rigid bodies con-nected 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. I.
CopyMe3D: Scanning and Printing Persons in 3D
"... Abstract. In this paper, we describe a novel approach to create 3D miniatures of persons using a Kinect sensor and a 3D color printer. To achieve this, we acquire color and depth images while the person is rotating on a swivel chair. We represent the model with a signed distance function which is up ..."
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Abstract. In this paper, we describe a novel approach to create 3D miniatures of persons using a Kinect sensor and a 3D color printer. To achieve this, we acquire color and depth images while the person is rotating on a swivel chair. We represent the model with a signed distance function which is updated and visualized as the images are captured for immediate feedback. Our approach automatically fills small holes that stem from self-occlusions. To optimize the model for 3D printing, we extract a watertight but hollow shell to minimize the production costs. In extensive experiments, we evaluate the quality of the obtained models as a function of the rotation speed, the non-rigid deformations of a person during recording, the camera pose, and the resulting self-occlusions. Finally, we present a large number of reconstructions and fabricated figures to demonstrate the validity of our approach. 1
Real-time large scale dense RGB-D SLAM with volumetric fusion
"... We present a new SLAM system capable of producing high quality globally consistent surface reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achi ..."
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We present a new SLAM system capable of producing high quality globally consistent surface reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real-time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an “as-rigid-as-possible ” space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.
Robust Reconstruction of Indoor Scenes
"... We present an approach to indoor scene reconstruction from RGB-D video. The key idea is to combine geomet-ric registration of scene fragments with robust global opti-mization based on line processes. Geometric registration is error-prone due to sensor noise, which leads to alias-ing of geometric det ..."
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We present an approach to indoor scene reconstruction from RGB-D video. The key idea is to combine geomet-ric registration of scene fragments with robust global opti-mization based on line processes. Geometric registration is error-prone due to sensor noise, which leads to alias-ing of geometric detail and inability to disambiguate dif-ferent surfaces in the scene. The presented optimization ap-proach disables erroneous geometric alignments even when they significantly outnumber correct ones. Experimental re-sults demonstrate that the presented approach substantially increases the accuracy of reconstructed scene models. 1.
Large-scale and drift-free surface reconstruction using online subvolume registration
- In CVPR, 2015. 8
"... Depth cameras have helped commoditize 3D digitization of the real-world. It is now feasible to use a single Kinect-like camera to scan in an entire building or other large-scale scenes. At large scales, however, there is an inherent chal-lenge of dealing with distortions and drift due to accumu-late ..."
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Depth cameras have helped commoditize 3D digitization of the real-world. It is now feasible to use a single Kinect-like camera to scan in an entire building or other large-scale scenes. At large scales, however, there is an inherent chal-lenge of dealing with distortions and drift due to accumu-lated pose estimation errors. Existing techniques suffer from one or more of the following: a) requiring an expensive offline global optimization step taking hours to compute; b) needing a full second pass over the input depth frames to correct for accumulated errors; c) relying on RGB data alongside depth data to optimize poses; or d) requiring the user to create explicit loop closures to allow gross alignment errors to be resolved. In this paper, we present a method that addresses all of these issues. Our method supports online model correction, without needing to reprocess or store any input depth data. Even while performing global correction of a large 3D model, our method takes only minutes rather than hours to compute. Our model does not require any explicit loop closures to be detected and, finally, relies on depth data alone, allowing operation in low-lighting conditions. We show qualitative results on many large scale scenes, high-lighting the lack of error and drift in our reconstructions. We compare to state of the art techniques and demonstrate large-scale dense surface reconstruction “in the dark”, a capability not offered by RGB-D techniques. 1.
CHISEL: Real Time Large Scale 3D Reconstruction Onboard a Mobile Device using Spatially-Hashed Signed Distance Fields
"... Abstract—We describe CHISEL: a system for real-time house-scale (300 square meter or more) dense 3D reconstruction onboard a Google Tango [1] mobile device by using a dynamic spatially-hashed truncated signed distance field[2] for mapping, and visual-inertial odometry for localization. By aggressive ..."
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Abstract—We describe CHISEL: a system for real-time house-scale (300 square meter or more) dense 3D reconstruction onboard a Google Tango [1] mobile device by using a dynamic spatially-hashed truncated signed distance field[2] for mapping, and visual-inertial odometry for localization. By aggressively culling parts of the scene that do not contain surfaces, we avoid needless computation and wasted memory. Even under very noisy conditions, we produce high-quality reconstructions through the use of space carving. We are able to reconstruct and render very large scenes at a resolution of 2-3 cm in real time on a mobile device without the use of GPU computing. The user is able to view and interact with the reconstruction in real-time through an intuitive interface. We provide both qualitative and quantitative results on publicly available RGB-D datasets [3], and on datasets collected in real-time from two devices. I.
Mathematical
"... Abstract—One of the major research areas in computer vision is scene reconstruction from image streams. The advent of RGB-D cameras, such as the Microsoft Kinect, has lead to new possibili-ties for performing accurate and dense 3D reconstruction. There are already well-working algorithms to acquire ..."
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Abstract—One of the major research areas in computer vision is scene reconstruction from image streams. The advent of RGB-D cameras, such as the Microsoft Kinect, has lead to new possibili-ties for performing accurate and dense 3D reconstruction. There are already well-working algorithms to acquire 3D models from depth sensors, both for large and small scale scenes. However, these methods often break down when the scene geometry is not so informative, for example, in the case of planar surfaces. Similarly, standard image-based methods fail for texture-less scenes. We combine both color and depth measurements from an RGB-D sensor to simultaneously reconstruct both the camera motion and the scene geometry in a robust manner. Experiments on real data show that we can accurately reconstruct large-scale 3D scenes despite many planar surfaces. 1 I.
Motion Segmentation of Truncated Signed Distance Function based Volumetric Surfaces
"... Abstract Truncated signed distance function (TSDF) ..."
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Direct Camera Pose Tracking and Mapping With Signed Distance Functions
"... Abstract—In many areas, the ability to create accurate 3D models is of great interest, for example, in computer vision, robotics, architecture, and augmented reality. In this paper we show how a textured indoor environment can be reconstructed in 3D using an RGB-D camera. Real-time performance can b ..."
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Abstract—In many areas, the ability to create accurate 3D models is of great interest, for example, in computer vision, robotics, architecture, and augmented reality. In this paper we show how a textured indoor environment can be reconstructed in 3D using an RGB-D camera. Real-time performance can be achieved using a GPU. We show how the camera pose can be estimated directly using the geometry that we represent as a signed distance function (SDF). Since the SDF contains information about the distance to the surface, it defines an error-metric which is minimized to estimate the pose of the camera. By iteratively estimating the camera pose and integrating the new depth images into the model, the 3D reconstruction is computed on the fly. We present several examples of 3D reconstructions made from a handheld and robot-mounted depth sensor, including detailed reconstructions from medium-sized rooms with almost drift-free pose estimation. Furthermore, we demonstrate that our algorithm is robust enough for 3D reconstruction using data recorded from a quadrocopter, making it potentially useful for navigation applications. I.
