About: 3D reconstruction is a(n) research topic. Over the lifetime, 7705 publication(s) have been published within this topic receiving 133011 citation(s). The topic is also known as: three-Dimensional Imaging & imaging, three-dimensional.
Papers published on a yearly basis
01 Jan 2001
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TL;DR: This paper describes the Semi-Global Matching (SGM) stereo method, which uses a pixelwise, Mutual Information based matching cost for compensating radiometric differences of input images and demonstrates a tolerance against a wide range of radiometric transformations.
Abstract: This paper describes the semiglobal matching (SGM) stereo method. It uses a pixelwise, mutual information (Ml)-based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement, and multibaseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments, and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed. A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2 seconds on typical test images. An in depth evaluation of the Ml-based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.
••16 Oct 2011
TL;DR: Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction, to enable real-time multi-touch interactions anywhere.
Abstract: KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to track the 3D pose of the sensor and reconstruct, geometrically precise, 3D models of the physical scene in real-time. The capabilities of KinectFusion, as well as the novel GPU-based pipeline are described in full. Uses of the core system for low-cost handheld scanning, and geometry-aware augmented reality and physics-based interactions are shown. Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction. These extensions are used to enable real-time multi-touch interactions anywhere, allowing any planar or non-planar reconstructed physical surface to be appropriated for touch.
01 Jan 1998
TL;DR: The development of the first core module in this effort: a 4-degree of freedom color object tracker and its application to flesh-tone-based face tracking and the development of a robust nonparametric technique for climbing density gradients to find the mode of a color distribution within a video scene.
Abstract: As a first step towards a perceptual user interface, a computer vision color tracking algorithm is developed and applied towards tracking human faces. Computer vision algorithms that are intended to form part of a perceptual user interface must be fast and efficient. They must be able to track in real time yet not absorb a major share of computational resources: other tasks must be able to run while the visual interface is being used. The new algorithm developed here is based on a robust nonparametric technique for climbing density gradients to find the mode (peak) of probability distributions called the mean shift algorithm. In our case, we want to find the mode of a color distribution within a video scene. Therefore, the mean shift algorithm is modified to deal with dynamically changing color probability distributions derived from video frame sequences. The modified algorithm is called the Continuously Adaptive Mean Shift (CAMSHIFT) algorithm. CAMSHIFT’s tracking accuracy is compared against a Polhemus tracker. Tolerance to noise, distractors and performance is studied. CAMSHIFT is then used as a computer interface for controlling commercial computer games and for exploring immersive 3D graphic worlds. Introduction This paper is part of a program to develop a Perceptual User Interface for computers. Perceptual interfaces are ones in which the computer is given the ability to sense and produce analogs of the human senses, such as allowing computers to perceive and produce localized sound and speech, giving computers a sense of touch and force feedback, and in our case, giving computers an ability to see. The work described in this paper is part of a larger effort aimed at giving computers the ability to segment, track, and understand the pose, gestures, and emotional expressions of humans and the tools they might be using in front of a computer or settop box. In this paper we describe the development of the first core module in this effort: a 4-degree of freedom color object tracker and its application to flesh-tone-based face tracking. Computer vision face tracking is an active and developing field, yet the face trackers that have been developed are not sufficient for our needs. Elaborate methods such as tracking contours with snakes [], using Eigenspace matching techniques , maintaining large sets of statistical hypotheses , or convolving images with feature detectors  are far too computationally expensive. We want a tracker that will track a given face in the presence of noise, other faces, and hand movements. Moreover, it must run fast and efficiently so that objects may be tracked in real time (30 frames per second) while consuming as few system resources as possible. In other words, this tracker should be able to serve as part of a user interface that is in turn part of the computational tasks that a computer might routinely be expected to carry out. This tracker also needs to run on inexpensive consumer cameras and not require calibrated lenses. In order, therefore, to find a fast, simple algorithm for basic tracking, we have focused on color-based tracking [], yet even these simpler algorithms are too computationally complex (and therefore slower at any given CPU speed) due to their use of color correlation, blob and region growing, Kalman filter smoothing and prediction, and contour considerations. The complexity of the these algorithms derives from their attempts to deal with irregular object motion due to perspective (near objects to the camera seem to move faster than distal objects); image noise; distractors, such as other faces in the scene; facial occlusion by hands or other objects; and lighting variations. We want a fast, computationally efficient algorithm that handles these problems in the course of its operation, i.e., an algorithm that mitigates the above problems “for free.” To develop such an algorithm, we drew on ideas from robust statistics and probability distributions. Robust statistics are those that tend to ignore outliers in the data (points far away from the region of interest). Thus, robust Intel Technology Journal Q2 ‘98 Computer Vision Face Tracking For Use in a Perceptual User Interface 2 algorithms help compensate for noise and distractors in the vision data. We therefore chose to use a robust nonparametric technique for climbing density gradients to find the mode of probability distributions called the mean shift algorithm . (The mean shift algorithm was never intended to be used as a tracking algorithm, but it is quite effective in this role.) The mean shift algorithm operates on probability distributions. To track colored objects in video frame sequences, the color image data has to be represented as a probability distribution ; we use color histograms to accomplish this. Color distributions derived from video image sequences change over time, so the mean shift algorithm has to be modified to adapt dynamically to the probability distribution it is tracking. The new algorithm that meets all these requirements is called CAMSHIFT. For face tracking, CAMSHIFT tracks the X, Y, and Area of the flesh color probability distribution representing a face. Area is proportional to Z, the distance from the camera. Head roll is also tracked as a further degree of freedom. We then use the X, Y, Z, and Roll derived from CAMSHIFT face tracking as a perceptual user interface for controlling commercial computer games and for exploring 3D graphic virtual worlds. Choose initial search window size and location HSV Image Set calculation region at search window center but larger in size than the search window Color histogram lookup in calculation region Color probability distribution Find center of mass within the search window Center search window at the center of mass and find area under it Converged YES NO Report X, Y, Z, and Roll Use (X,Y) to set search window center, 2*area 1/2
••08 Oct 2016
Abstract: Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2). The network learns a mapping from images of objects to their underlying 3D shapes from a large collection of synthetic data . Our network takes in one or more images of an object instance from arbitrary viewpoints and outputs a reconstruction of the object in the form of a 3D occupancy grid. Unlike most of the previous works, our network does not require any image annotations or object class labels for training or testing. Our extensive experimental analysis shows that our reconstruction framework (i) outperforms the state-of-the-art methods for single view reconstruction, and (ii) enables the 3D reconstruction of objects in situations when traditional SFM/SLAM methods fail (because of lack of texture and/or wide baseline).