View planning for automated three-dimensional object reconstruction and inspection

Scott, William; Roth, Gerhard; Rivest, Jean-François

doi:10.1145/641865.641868

Cited by 415 publications

(292 citation statements)

References 53 publications

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“…The problem of computing the optimal views to reconstruct an object or a scene has been studied for more than two decades and is known in the computer vision literature as active vision, View Path Planning (VPP), or Next-Best-View (NBV) [1,2,4,7,21]. Often, the sensor motion is restricted to a sphere and it is assumed that the object of interest is at all times located completely in the sensor frustum.…”

Section: A Related Workmentioning

confidence: 99%

Appearance-based Active, Monocular, Dense Reconstruction for Micro Aerial Vehicles

Förster

Pizzoli

Scaramuzza

2014

Robotics: Science and Systems X

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Abstract-In this paper, we investigate the following problem: given the image of a scene, what is the trajectory that a robotmounted camera should follow to allow optimal dense depth estimation? The solution we propose is based on maximizing the information gain over a set of candidate trajectories. In order to estimate the information that we expect from a camera pose, we introduce a novel formulation of the measurement uncertainty that accounts for the scene appearance (i.e., texture in the reference view), the scene depth and the vehicle pose. We successfully demonstrate our approach in the case of realtime, monocular reconstruction from a micro aerial vehicle and validate the effectiveness of our solution in both synthetic and real experiments. To the best of our knowledge, this is the first work on active, monocular dense reconstruction, which chooses motion trajectories that minimize perceptual ambiguities inferred by the texture in the scene.

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Section: A Related Workmentioning

confidence: 99%

Appearance-based Active, Monocular, Dense Reconstruction for Micro Aerial Vehicles

Förster

Pizzoli

Scaramuzza

2014

Robotics: Science and Systems X

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“…In fact, we refer to the visual hull that is constructed from images of cameras from different viewpoints. This problem can be seen as a view planning problem (see [19], a survey of computer vision sensor planning, [18], a more recent survey of view planning for 3-D vision). In our case, [9], starting from a compact block of voxels, each time a camera is added, a set of voxels are deleted (carved) from the 3D reconstruction, so the sequence of 3D reconstructions along decreasing number of cameras gives place to a filter of the corresponding cubical complexes.…”

Section: Lemma 1 the Number Of Intervals In Anmentioning

confidence: 99%

Towards Minimal Barcodes

González-Dı́az

Jiménez

Krim³

2013

Graph-Based Representations in Pattern Recognition

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Abstract. In the setting of persistent homology computation, a useful tool is the persistence barcode representation in which pairs of birth and death times of homology classes are encoded in the form of intervals. Starting from a polyhedral complex K (an object subdivided into cells which are polytopes) and an initial order of the set of vertices, we are concerned with the general problem of searching for filters (an order of the rest of the cells) that provide a minimal barcode representation in the sense of having minimal number of "k-significant" intervals, which correspond to homology classes with life-times longer than a fixed number k. As a first step, in this paper we provide an algorithm for computing such a filter for k = 1 on the Hasse diagram of the poset of faces of K.

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“…Sensor view planning has been commonly used for the tasks of precise geometrical model construction and object recognition (see the reviews [18] and [15]), and to a lesser extent for the optimal segmentation of particular object characteristics [11,16] and to exploit sensor features to easily detect occlusions, formerly using a laser [12] and more recently with a ToF sensor [6]. These algorithms can be classified according to the constraints they impose, on the type of objects that can handle, the sensors they use, the restrictions of the sensor positioning system, and more important, the decisionmaking strategy and the symbolic object representation they used.…”

Section: Related Workmentioning

confidence: 99%

Information-Gain View Planning for Free-Form Object Reconstruction with a 3D ToF Camera

Foix

Kriegel

Fuchs

et al. 2012

Advanced Concepts for Intelligent Vision Systems

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Abstract. Active view planning for gathering data from an unexplored 3D complex scenario is a hard and still open problem in the computer vision community. In this paper, we present a general task-oriented approach based on an information-gain maximization that easily deals with such a problem. Our approach consists of ranking a given set of possible actions, based on their taskrelated gains, and then executing the best-ranked action to move the required sensor. An example of how our approach behaves is demonstrated by applying it over 3D raw data for real-time volume modelling of complex-shaped objects. Our setting includes a calibrated 3D time-of-flight (ToF) camera mounted on a 7 degrees of freedom (DoF) robotic arm. Noise in the sensor data acquisition, which is too often ignored, is here explicitly taken into account by computing an uncertainty matrix for each point, and refining this matrix each time the point is seen again. Results show that, by always choosing the most informative view, a complete model of a 3D free-form object is acquired and also that our method achieves a good compromise between speed and precision.

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View planning for automated three-dimensional object reconstruction and inspection

Cited by 415 publications

References 53 publications

Appearance-based Active, Monocular, Dense Reconstruction for Micro Aerial Vehicles

Appearance-based Active, Monocular, Dense Reconstruction for Micro Aerial Vehicles

Towards Minimal Barcodes

Information-Gain View Planning for Free-Form Object Reconstruction with a 3D ToF Camera

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