Shape from shading using linear approximation

doi:10.1016/0262-8856(94)90002-7

Image and Vision Computing

Volume 12, Issue 8, October 1994, Pages 487-498

https://doi.org/10.1016/0262-8856(94)90002-7 Get rights and content

Abstract

In this paper, we present an extremely simple algorithm for shape from shading, which can be implemented in 25 lines of C code^∗. The algorithm is very fast, taking 0.2 seconds on a Sun SparcStation-1 for a 128 × 128 image, and is purely local and highly parallelizable (parallel implementation included). In our approach, we employ a linear approximation of the reflectance function, as used by others. However, the main difference is that we first use the discrete approximations for surface normal, p and q, using finite differences, and then linearize the reflectance function in depth, Z(x, y), instead of p and q. The algorithm has been tested on several synthetic and real images of both Lambertian and specular surfaces, and good results have been obtained.

References (12)

CH Lee et al.
Improved methods of estimating shape from shading using the light source coordinate system
Artif. Intell.
(1985)
J Oliensis
Shape from shading as a partically well-constrained problem
Comput. Vision, Graph. Image Process.: Image Understanding
(September 1991)
G Healey et al.
Local shape from specularity
Comput. Vision, Graph. Image Process.
(1988)
BKP Horn
Height and gradient from shading
Int. J. Comput. Vision.
(1990)
A Pentland
Shape information from shading: a theory about human perception
A Pentland
Finding the illuminant direction
J Opt. Soc. Am.
(1982)

There are more references available in the full text version of this article.

Cited by (281)

Wear depth estimation from single 2-D image based on shape from Shading and convolutional neural network hybrid model for in-situ wear assessment
2024, Wear
Vision-based wear inspection is a prevalent technique in in-situ assessment, such as borescope inspection. Evaluating wear volume of the damage has relied on Shape From Shading (SFS) algorithms to reconstruct the 3-D topography from a single 2-D image. However, this approach faces limitations in accuracy due to data insufficiency and the impact of non-Lambertian reflections on a 2-D surface image. To address this issue, a depth estimation method is proposed, centered on an SFS-Convolutional Neural Network (CNN) hybrid model. First, in terms of the insufficient information of a 2-D image, a coarse-to-fine detection network has been developed to identify the damage region. Regarding the influence of the non-Lambert reflection of the worn surface, an SFS-CNN hybrid model is constructed incorporating an SFS-based guidance branch and a CNN branch based on a dual-attention mechanism. Specifically, the latter emphasizes the connection between the 2-D damage image and the depth information, disregarding non-Lambertian reflection, while the former extracts the initial depth information from the damage region for CNN learning. Consequently, the wear volume can be derived by accumulating the depth of the reconstructed 3-D worn region. To verify, several real worn surfaces have been used and examined using the proposed model and Laser Scanning Confocal Microscopy. Experimental results indicate that the proposed method can determine the depth of a worn surface from a single surface image, and the absolute relative error of the computed wear volume is less than 19 %, which satisfies the requirement of in-situ wear condition assessment.
Motion-based camera localization system in colonoscopy videos
2021, Medical Image Analysis
Optical colonoscopy is an essential diagnostic and prognostic tool for many gastrointestinal diseases, including cancer screening and staging, intestinal bleeding, diarrhea, abdominal symptom evaluation, and inflammatory bowel disease assessment. However, the evaluation, classification, and quantification of findings from colonoscopy are subject to inter-observer variation. Automated assessment of colonoscopy is of interest considering the subjectivity present in qualitative human interpretations of colonoscopy findings. Localization of the camera is essential to interpreting the meaning and context of findings for diseases evaluated by colonoscopy. In this study, we propose a camera localization system to estimate the relative location of the camera and classify the colon into anatomical segments. The camera localization system begins with non-informative frame detection and removal. Then a self-training end-to-end convolutional neural network is built to estimate the camera motion, where several strategies are proposed to improve its robustness and generalization on endoscopic videos. Using the estimated camera motion a camera trajectory can be derived and a relative location index calculated. Based on the estimated location index, anatomical colon segment classification is performed by constructing a colon template. The proposed motion estimation algorithm was evaluated on an external dataset containing the ground truth for camera pose. The experimental results show that the performance of the proposed method is superior to other published methods. The relative location index estimation and anatomical region classification were further validated using colonoscopy videos collected from routine clinical practice. This validation yielded an average accuracy in classification of 0.754, which is substantially higher than the performances obtained using location indices built from other methods.
EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos
2021, Medical Image Analysis
Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not support effective quantitative benchmarking. In this paper, we introduce a comprehensive endoscopic SLAM dataset consisting of 3D point cloud data for six porcine organs, capsule and standard endoscopy recordings, synthetically generated data as well as clinically in use conventional endoscope recording of the phantom colon with computed tomography(CT) scan ground truth. A Panda robotic arm, two commercially available capsule endoscopes, three conventional endoscopes with different camera properties, two high precision 3D scanners, and a CT scanner were employed to collect data from eight ex-vivo porcine gastrointestinal (GI)-tract organs and a silicone colon phantom model. In total, 35 sub-datasets are provided with 6D pose ground truth for the ex-vivo part: 18 sub-datasets for colon, 12 sub-datasets for stomach, and 5 sub-datasets for small intestine, while four of these contain polyp-mimicking elevations carried out by an expert gastroenterologist. To verify the applicability of this data for use with real clinical systems, we recorded a video sequence with a state-of-the-art colonoscope from a full representation silicon colon phantom. Synthetic capsule endoscopy frames from stomach, colon, and small intestine with both depth and pose annotations are included to facilitate the study of simulation-to-real transfer learning algorithms. Additionally, we propound Endo-SfMLearner, an unsupervised monocular depth and pose estimation method that combines residual networks with a spatial attention module in order to dictate the network to focus on distinguishable and highly textured tissue regions. The proposed approach makes use of a brightness-aware photometric loss to improve the robustness under fast frame-to-frame illumination changes that are commonly seen in endoscopic videos. To exemplify the use-case of the EndoSLAM dataset, the performance of Endo-SfMLearner is extensively compared with the state-of-the-art: SC-SfMLearner, Monodepth2, and SfMLearner. The codes and the link for the dataset are publicly available at https://github.com/CapsuleEndoscope/EndoSLAM. A video demonstrating the experimental setup and procedure is accessible as Supplementary Video 1.
Spoof-Guided Image Decomposition for Face Anti-spoofing
2024, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Mesh Refinement Combined with Shape From Shading
2023, Laser and Optoelectronics Progress
Shape recovery from fusion of polarization binocular vision and shading
2023, Applied Optics

View all citing articles on Scopus

^∗: C code and some images can be obtained by anonymous ftp from under the directory /pub/shading.

View full text

Shape from shading using linear approximation

Abstract

Artif. Intell.

Comput. Vision, Graph. Image Process.: Image Understanding

Comput. Vision, Graph. Image Process.

Height and gradient from shading

Int. J. Comput. Vision.

Shape information from shading: a theory about human perception

Finding the illuminant direction

J Opt. Soc. Am.