Parallel refinement of slanted 3D reconstruction using dense stereo induced from symmetry

Falcão, Gabriel; Amaro, João; Mota, Vasco; Antunes, M.; Barreto, João Pedro; Nunes, Urbano

doi:10.1007/s11554-016-0592-0

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…In the case of monocular cameras, it is very difficult to reconstruct the shape of an object because there are no good constraints between the available cues, and there is no perfect solution to the problem of 3D reconstruction for monocular cameras [2]. In the case of multicamera, since the cues obtained between the cameras can form better constraints, including the reconstruction of the 3D model from the contour with volume intersection or with multicamera stereo vision techniques, generalists applying sophisticated 3D scanning devices or 3D reconstruction algorithms can capture fine human shapes, often with millimeter-level accuracy [3].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

Dai¹

2021

Complexity

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Computer vision is widely used in manufacturing, sports, medical diagnosis, and other fields. In this article, a multifeature fusion error action expression method based on silhouette and optical flow information is proposed to overcome the shortcomings in the effectiveness of a single error action expression method based on the fusion of features for human body error action recognition. We analyse and discuss the human error action recognition method based on the idea of template matching to analyse the key issues that affect the overall expression of the error action sequences, and then, we propose a motion energy model based on the direct motion energy decomposition of the video clips of human error actions in the 3 Deron action sequence space through the filter group. The method can avoid preprocessing operations such as target localization and segmentation; then, we use MET features and combine with SVM to test the human body error database and compare the experimental results obtained by using different feature reduction and classification methods, and the results show that the method has the obvious comparative advantage in the recognition rate and is suitable for other dynamic scenes.

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Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

Dai¹

2021

Complexity

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“…Shahram Izadi et al constructed the KinectFusion system [16] and used Kinect to reconstruct small-scale scenes and individual objects in three dimensions. The system uses a truncated signed distance function (TSDF) map [5,8,[26][27][28][29][30][31][32][33]. Compared with the method of Henry et al [15], the results are meticulous.…”

Section: Introductionmentioning

confidence: 99%

Interior dense 3D reconstruction system with RGB-D camera for complex large scenes

Fu¹,

Li²,

Yu³

2020

Meas. Sci. Technol.

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At present, a 3D reconstruction system with simultaneous localization and mapping (SLAM) based on the feature point method presents critical difficulties when the texture is missing. In contrast, with the SLAM based on the direct method, unsatisfactory reconstruction results are achieved when the camera moves at a high speed due to the difficulty in pose estimation. In order to solve such problems, this paper presents a dense 3D scene reconstruction system with a depth camera (RGB-D camera) based on semi-direct SLAM. The system uses the feature point method to estimate the pose of the camera in the rich region of the texture, and then uses an efficient incremental bundle adjustment to optimize the pose of the camera. In areas where the texture is missing, the direct method is used to estimate the pose of the camera. Therefore, the photometric error can be reduced when optimizing the pose of the camera. Then, a 3D map is constructed using the optimized camera pose. The surfel model and the deformation map are used to estimate the pose of the point cloud and the fusion point cloud. The 3D reconstruction system presents the following characteristics: (1) A hand-held camera can be used to scan for 3D reconstruction with any gesture, where the system can reduce the error of reconstruction model caused by human operation; (2) high robustness, stability and strength to deal with the jitter of the missing area and the camera; (3) dense reconstruction of a large scene can be performed, and the reconstruction effect can be well obtained. Multiple experiments show that the proposed system can be applied to 3D reconstruction of various typologies, and can get the optimal 3D reconstruction model. The obtained results prove its applicability in robot navigation, virtual reality shopping malls and other fields.

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GPU-accelerated uncapacitated facility location and semi-dense SymStereo pipelines for piecewise-planar-based 3D reconstruction

Graca

Raposo

Barreto

et al. 2020

J Real-Time Image Proc

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Parallel refinement of slanted 3D reconstruction using dense stereo induced from symmetry

Cited by 6 publications

References 33 publications

Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

Interior dense 3D reconstruction system with RGB-D camera for complex large scenes

GPU-accelerated uncapacitated facility location and semi-dense SymStereo pipelines for piecewise-planar-based 3D reconstruction

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