Scene Structure

2021 ◽  
pp. 54-56
Author(s):  
Jacqueline Goldfinger
Keyword(s):  
2021 ◽  
Vol 13 (11) ◽  
pp. 2145
Author(s):  
Yawen Liu ◽  
Bingxuan Guo ◽  
Xiongwu Xiao ◽  
Wei Qiu

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.


1992 ◽  
Vol 337 (1281) ◽  
pp. 341-350 ◽  

Localized feature points, particularly corners, can be computed rapidly and reliably in images, and they are stable over image sequences. Corner points provide more constraint than edge points, and this additional constraint can be propagated effectively from corners along edges. Implemented algorithms are described to compute optic flow and to determine scene structure for a mobile robot using stereo or structure from motion. It is argued that a mobile robot may not need to compute depth explicitly in order to navigate effectively.


2009 ◽  
Vol 30 (3) ◽  
pp. 192-202 ◽  
Author(s):  
Guanghui Wang ◽  
Hung-Tat Tsui ◽  
Q.M. Jonathan Wu

2001 ◽  
Vol 01 (03) ◽  
pp. 507-526 ◽  
Author(s):  
TONG LIN ◽  
HONG-JIANG ZHANG ◽  
QING-YUN SHI

In this paper, we present a novel scheme on video content representation by exploring the spatio-temporal information. A pseudo-object-based shot representation containing more semantics is proposed to measure shot similarity and force competition approach is proposed to group shots into scene based on content coherences between shots. Two content descriptors, color objects: Dominant Color Histograms (DCH) and Spatial Structure Histograms (SSH), are introduced. To represent temporal content variations, a shot can be segmented into several subshots that are of coherent content, and shot similarity measure is formulated as subshot similarity measure that serves to shot retrieval. With this shot representation, scene structure can be extracted by analyzing the splitting and merging force competitions at each shot boundary. Experimental results on real-world sports video prove that our proposed approach for video shot retrievals achieve the best performance on the average recall (AR) and average normalized modified retrieval rank (ANMRR), and Experiment on MPEG-7 test videos achieves promising results by the proposed scene extraction algorithm.


Author(s):  
Dimitrios Chrysostomou ◽  
Antonios Gasteratos

The production of 3D models has been a popular research topic already for a long time, and important progress has been made since the early days. During the last decades, vision systems have established to become the standard and one of the most efficient sensorial assets in industrial and everyday applications. Due to the fact that vision provides several vital attributes, many applications tend to use novel vision systems into domestic, working, industrial, and any other environments. To achieve such goals, a vision system should robustly and effectively reconstruct the 3D surface and the working space. This chapter discusses different methods for capturing the three-dimensional surface of a scene. Geometric approaches to three-dimensional scene reconstruction are generally based on the knowledge of the scene structure from the camera’s internal and external parameters. Another class of methods encompasses the photometric approaches, which evaluate the pixels’ intensity to understand the three-dimensional scene structure. The third and final category of approaches, the so-called real aperture approaches, includes methods that use the physical properties of the visual sensors for image acquisition in order to reproduce the depth information of a scene.


2020 ◽  
Vol 6 (10) ◽  
pp. eaax5979 ◽  
Author(s):  
Ilker Yildirim ◽  
Mario Belledonne ◽  
Winrich Freiwald ◽  
Josh Tenenbaum

Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or “analysis-by-synthesis”, presents a possible solution, but its mechanistic implementations have typically been too slow for online perception, and their mapping to neural circuits remains unclear. Here we present a neurally plausible efficient inverse graphics model and test it in the domain of face recognition. The model is based on a deep neural network that learns to invert a three-dimensional face graphics program in a single fast feedforward pass. It explains human behavior qualitatively and quantitatively, including the classic “hollow face” illusion, and it maps directly onto a specialized face-processing circuit in the primate brain. The model fits both behavioral and neural data better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how the brain transforms images into percepts.


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