INVESTIGATING 3D RECONSTRUCTION OF NON-COLLABORATIVE SURFACES THROUGH PHOTOGRAMMETRY AND PHOTOMETRIC STEREO

Abstract. 3D digital reconstruction techniques are extensively used for quality control purposes. Among them, photogrammetry and photometric stereo methods have been for a long time used with success in several application fields. However, generating highly-detailed and reliable micro-measurements of non-collaborative surfaces is still an open issue. In these cases, photogrammetry can provide accurate low-frequency 3D information, whereas it struggles to extract reliable high-frequency details. Conversely, photometric stereo can recover a very detailed surface topography, although global surface deformation is often present. In this paper, we present the preliminary results of an ongoing project aiming to combine photogrammetry and photometric stereo in a synergetic fusion of the two techniques. Particularly, hereafter, we introduce the main concept design behind an image acquisition system we developed to capture images from different positions and under different lighting conditions as required by photogrammetry and photometric stereo techniques. We show the benefit of such a combination through some experimental tests. The experiments showed that the proposed method recovers the surface topography at the same high-resolution achievable with photometric stereo while preserving the photogrammetric accuracy. Furthermore, we exploit light directionality and multiple light sources to improve the quality of dense image matching in poorly textured surfaces

An improved photometric stereo through distance estimation and light vector optimization from diffused maxima region

© 2013 Elsevier B.V. All rights reserved. Although photometric stereo offers an attractive technique for acquiring 3D data using low-cost equipment, inherent limitations in the methodology have served to limit its practical application, particularly in measurement or metrology tasks. Here we address this issue. Traditional photometric stereo assumes that lighting directions at every pixel are the same, which is not usually the case in real applications, and especially where the size of object being observed is comparable to the working distance. Such imperfections of the illumination may make the subsequent reconstruction procedures used to obtain the 3D shape of the scene prone to low frequency geometric distortion and systematic error (bias). Also, the 3D reconstruction of the object results in a geometric shape with an unknown scale. To overcome these problems a novel method of estimating the distance of the object from the camera is developed, which employs photometric stereo images without using other additional imaging modality. The method firstly identifies Lambertian diffused maxima region to calculate the object distance from the camera, from which the corrected per-pixel light vector is able to be derived and the absolute dimensions of the object can be subsequently estimated. We also propose a new calibration process to allow a dynamic(as an object moves in the field of view) calculation of light vectors for each pixel with little additional computation cost. Experiments performed on synthetic as well as real data demonstrates that the proposed approach offers improved performance, achieving a reduction in the estimated surface normal error of up to 45% as well as mean height error of reconstructed surface of up to 6 mm. In addition, when compared to traditional photometric stereo, the proposed method reduces the mean angular and height error so that it is low, constant and independent of the position of the object placement within a normal working range

Innovative optical non-contact measurement of respiratory function using photometric stereo

Pulmonary functional testing is very common and widely used in today's clinical environment for testing lung function. The contact based nature of a Spirometer can cause breathing awareness that alters the breathing pattern, affects the amount of air inhaled and exhaled and has hygiene implications. Spirometry also requires a high degree of compliance from the patient, as they have to breathe through a hand held mouth piece. To solve these issues a non-contact computer vision based system was developed for Pulmonary Functional Testing. This employs an improved photometric stereo method that was developed to recover local 3D surface orientation to enable calculation of breathing volumes. Although Photometric Stereo offers an attractive technique for acquiring 3D data using low-cost equipment, inherent limitations in the methodology have served to limit its practical application, particularly in measurement or metrology tasks. Traditional Photometric Stereo assumes that lighting directions at every pixel are the same, which is not usually the case in real applications and especially where the size of object being observed is comparable to the working distance. Such imperfections of the illumination may make the subsequent reconstruction procedures used to obtain the 3D shape of the scene, prone to low frequency geometric distortion and systematic error (bias). Also, the 3D reconstruction of the object results in a geometric shape with an unknown scale. To overcome these problems a novel method of estimating the distance of the object from the camera was developed, which employs Photometric Stereo images without using other additional imaging modality. The method firstly identifies the Lambertian Diffused Maxima regions to calculate the object's distance from the camera, from which the corrected per-pixel light vector is derived and the absolute dimensions of the object can be subsequently estimated. We also propose a new calibration process to allow a dynamic (as an object moves in the field of view) calculation of light vectors for each pixel with little additional computational cost. Experiments performed on synthetic as well as real data demonstrate that the proposed approach offers improved performance, achieving a reduction in the estimated surface normal error by up to 45% as well as the mean height error of reconstructed surface of up to 6 mm. In addition, compared with traditional photometric stereo, the proposed method reduces the mean angular and height error so that it is low, constant and independent of the position of the object placement within a normal working range. A high (0.98) correlation between breathing volume calculated from Photometric Stereo and Spirometer data was observed. This breathing volume is then converted to absolute amount of air by using distance information obtained by Lambertian Diffused Maxima Region. The unique and novel feature of this system is that it views the patients from both front and back and creates a 3D structure of the whole torso. By observing the 3D structure of the torso over time, the amount of air inhaled and exhaled can be estimated

Identifying the lights position in photometric stereo under unknown lighting

Reconstructing the 3D shape of an object from a set of images is a classical problem in Computer Vision. Photometric stereo is one of the possible approaches. It stands on the assumption that the object is observed from a fixed point of view under different lighting conditions. The traditional approach requires that the position of the light sources is accurately known. It has been proved that the lights position can be estimated directly from the data, when at least 6 images of the observed object are available. In this paper, we give a Matlab implementation of the algorithm for solving the photometric stereo problem under unknown lighting, and propose a simple shooting technique to solve the bas-relief ambiguity.Comment: new versio

A Novel Framework for Highlight Reflectance Transformation Imaging

We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa

Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications

Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers. In many low-level vision problems, not only it is known that the underlying structure of clean data is low-rank, but the exact rank of clean data is also known. Yet, when applying conventional rank minimization for those problems, the objective function is formulated in a way that does not fully utilize a priori target rank information about the problems. This observation motivates us to investigate whether there is a better alternative solution when using rank minimization. In this paper, instead of minimizing the nuclear norm, we propose to minimize the partial sum of singular values, which implicitly encourages the target rank constraint. Our experimental analyses show that, when the number of samples is deficient, our approach leads to a higher success rate than conventional rank minimization, while the solutions obtained by the two approaches are almost identical when the number of samples is more than sufficient. We apply our approach to various low-level vision problems, e.g. high dynamic range imaging, motion edge detection, photometric stereo, image alignment and recovery, and show that our results outperform those obtained by the conventional nuclear norm rank minimization method.Comment: Accepted in Transactions on Pattern Analysis and Machine Intelligence (TPAMI). To appea