Analysis and Approximation of Some Shape-from-Shading Models for Non-Lambertian Surfaces

Tozza, Silvia; Falcone, Maurizio

doi:10.1007/s10851-016-0636-x

Cited by 25 publications

(23 citation statements)

References 71 publications

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“…The basic idea of SFS is that the illuminated patches of object surfaces correspond to the local regions in the gray image [29]. The gray scale of an image is mainly related to the following four factors: the surface orientation, the illumination direction, the direction of the camera relative to the object surface, and the reflection characteristics of the object surface, as shown in Figure 1.…”

Section: Basic Principle Of Sfsmentioning

confidence: 99%

Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints

Shi

Zheng

et al. 2019

Applied Sciences

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Featured Application: Applying on mechanical engineering. Abstract: In the modern engineering field, recovering the machined surface topography is important for studying mechanical product function and surface characteristics by using the shape from shading (SFS)-based reconstruction method. However, due to the limitations of many constraints and oversmoothing, the existing SFS-based reconstruction methods are not suitable for machined surface topography. This paper presents a new three-dimensional (3D) reconstruction method of machined surface topography. By combining the basic principle of SFS and the analytic method, the analytic model of a surface gradient is established using the gray gradient as a constraint condition. By efficiently solving the effect of quantization errors and ambiguity of the gray scale on reconstruction accuracy using a wavelet denoising algorithm and image processing technology, the reconstruction algorithm is implemented for machined surface topography. Experimental results on synthetic images and machined surface topography images show that the proposed algorithm can accurately and efficiently recover the 3D shape of machined surface topography.Appl. Sci. 2019, 9, 591 2 of 16 wear particle surfaces based on a photometric stereo, and obtained the surface topographies of wear particles for further feature-based wear particle identification. Tang et al. [9] used the 3D reconstruction method based on the shape from a focus to determine the grinding wheel surface topography.Three-dimensional reconstruction is the process of recovering 3D shapes of objects from single or multiple images [10][11][12], and the shape from shading (SFS) method is considered to be one of the fastest and most efficient 3D reconstruction methods [13][14][15]. Therefore, the SFS-based reconstruction method has attracted attention from numerous researchers. Maurer et al. [16] used a method that combined SFS and stereo to establish the functional energy with a detail-preserving anisotropic second-order smoothness term, and estimated the depth of the object surface. Lu et al. [17] proposed a Lambert-Phong hybrid model algorithm, and obtained the coordinate information of the highlighted region of the droplet surface by the mask regions with convolutional neural network (R-CNN). Furthermore, they used the Taylor expansion and Newton iteration method in the reflection model to obtain the heights of all the positions. Sun et al. [18] proposed a new roughness evaluation method based on the complex wavelet enhanced SFS transform, and recovered the 3D topography of a milling surface using the 3D models that were developed from the digital image. Zhu et al. [19] proposed a novel method that uses Fourier transform to fuse the depth from focus (DFF) and SFS, and achieved the reconstruction of the tool wear image in addition to ensuring the continuity of the depth map. Yang et al. [20] used the SFS algorithm to reconstruct the 3D shapes of the welding seam, and extracted the curvature information as the feature vector of the ...

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Section: Basic Principle Of Sfsmentioning

confidence: 99%

Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints

Shi

Zheng

et al. 2019

Applied Sciences

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“…However, the eye-in-hand vision system is more accurate than the visual odometer. Therefore, it is necessary to make full use of the high accuracy of the robotic manipulator to improve the quality of the 3D reconstruction of the scene [16,17]. Another problem is that the precision of semantic segmentation is still insufficient, even by the latest method, so it is necessary to find a way to improve the quality of semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System

Zha

Wang

et al. 2020

Applied Sciences

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Three-dimensional reconstruction and semantic understandings have attracted extensive attention in recent years. However, current reconstruction techniques mainly target large-scale scenes, such as an indoor environment or automatic self-driving cars. There are few studies on small-scale and high-precision scene reconstruction for manipulator operation, which plays an essential role in the decision-making and intelligent control system. In this paper, a group of images captured from an eye-in-hand vision system carried on a robotic manipulator are segmented by deep learning and geometric features and create a semantic 3D reconstruction using a map stitching method. The results demonstrate that the quality of segmented images and the precision of semantic 3D reconstruction are effectively improved by our method.

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“…Since the pioneering work of Horn, the majority of the SFS works focuses on how to build a numerical scheme with the simple Lambertian model; thus, many SFS algorithms have been reported (one can see the extensive list of the references in the three surveys [3][4][5]). According to Zhang et al [4], Durou et al [5], and Tozza and Falcone [6], these algorithms are roughly divided into two categories: PDE-based approaches and optimization approaches. The early work of Horn [1] based on the method of characteristics and the viscosity solution theories-based methods [7][8][9][10][11] all belong to the first category.…”

Section: Introductionmentioning

confidence: 99%

A New Oren–Nayar Shape-from-Shading Approach for 3D Reconstruction Using High-Order Godunov-Based Scheme

Wang

Chu

2018

Algorithms

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3D shape reconstruction from images has been an important topic in the field of robot vision. Shape-From-Shading (SFS) is a classical method for determining the shape of a 3D surface from a one intensity image. The Lambertian reflectance is a fundamental assumption in conventional SFS approaches. Unfortunately, when applied to characterize the reflection attribute of the diffuse reflection, the Lambertian model is tested to be inexact. In this paper, we present a new SFS approach for 3D reconstruction of diffuse surfaces whose reflection attribute is approximated by the Oren-Nayar reflection model. The partial differential Image Irradiance Equation (IIR) is set up with this model under a single distant point light source and an orthographic camera projection whose direction coincides with the light source. Then, the IIR is converted into an eikonal equation by solving a quadratic equation that includes the 3D surface shape. The viscosity solution of the resulting eikonal equation is approximated by using the high-order Godunov-based scheme that is accelerated by means of an alternating sweeping strategy. We conduct the experiments on synthetic and real-world images, and the experimental results illustrate the effectiveness of the presented approach.

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Analysis and Approximation of Some Shape-from-Shading Models for Non-Lambertian Surfaces

Cited by 25 publications

References 71 publications

Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints

Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints

Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System

A New Oren–Nayar Shape-from-Shading Approach for 3D Reconstruction Using High-Order Godunov-Based Scheme

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