A fast 3D reconstruction system with a low-cost camera accessory

Photometric stereo is a three dimensional (3D) imaging technique that uses multiple 2D images, obtained from a fixed camera perspective, with different illumination directions. Compared to other 3D imaging methods such as geometry modeling and 3D-scanning, it comes with a number of advantages, such as having a simple and efficient reconstruction routine. In this work, we describe a low-cost accessory to a commercial digital single-lens reflex (DSLR) camera system allowing fast reconstruction of 3D objects using photometric stereo. The accessory consists of four white LED lights fixed to the lens of a commercial DSLR camera and a USB programmable controller board to sequentially control the illumination. 3D images are derived for different objects with varying geometric complexity and results are presented, showing a typical height error of <3 mm for a 50 mm sized object

Résolution du problème de la stéréophotométrie non calibrée par estimation de l'intensité des éclairages

Nous présentons une nouvelle méthode de résolution du problème de la stéréophotométrie non calibrée dans le cadre lambertien, fondée sur l'hypothèse que tous les éclairages ont la même intensité, couplée à l'estimation de cette intensité. Nous montrons comment cette hypothèse permet de lever les ambiguïtés inhérentes à la linéarité du modèle lambertien, et notamment de résoudre l'ambiguïté de bas- relief. Le problème devenant alors bien posé, nous proposons une méthode complète pour résoudre le problème de la stéréophotométrie non calibrée et ainsi estimer conjoin- tement les conditions d'éclairage et le champ de normales. Nous validons notre méthode par la reconstruction 3D de visages à partir d'images réelles, et nous comparons son efficacité et sa précision aux techniques les plus récentes de la littérature

PS-FCN: A Flexible Learning Framework for Photometric Stereo

This paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an arbitrary number of images of a static object captured under different light directions with a fixed camera as input, and predicts a normal map of the object in a fast feed-forward pass. Unlike the recently proposed learning based method, PS-FCN does not require a pre-defined set of light directions during training and testing, and can handle multiple images and light directions in an order-agnostic manner. Although we train PS-FCN on synthetic data, it can generalize well on real datasets. We further show that PS-FCN can be easily extended to handle the problem of uncalibrated photometric stereo.Extensive experiments on public real datasets show that PS-FCN outperforms existing approaches in calibrated photometric stereo, and promising results are achieved in uncalibrated scenario, clearly demonstrating its effectiveness.Comment: ECCV 2018: https://guanyingc.github.io/PS-FC

Photometric stereo with auto-radiometric calibration

We propose a novel method for estimating surface nor-mals and a radiometric response function of a camera at the same time. Photometric stereo assumes that a camera is radiometrically calibrated in advance, so that image ir-radiance values can be determined from observed pixel val-ues. Our proposed method avoids such often cumbersome radiometric calibration of a camera by the simultaneous estimation of surface normals and a radiometric response function. The key idea of our method is to make use of the consistency; the irradiance values converted from pixel values by using the radiometric response function should be equal to the corresponding irradiance values calculated from the surface normals. We show that the simultaneous estimation results in a linear least-square problem with lin-ear constraints. The experimental results demonstrate that our method can estimate surface normals accurately even when images are captured by using cameras with nonlinear radiometric response functions. 1

Direct Differential Photometric Stereo Shape Recovery of Diffuse and Specular Surfaces

This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s10851-016-0633-0Recovering the 3D shape of an object from shading is a challenging problem due to the complexity of modeling light propagation and surface reflections. Photometric Stereo (PS) is broadly considered a suitable approach for high-resolution shape recovery, but its functionality is restricted to a limited set of object surfaces and controlled lighting setup. In particular, PS models generally consider reflection from objects as purely diffuse, with specularities being regarded as a nuisance that breaks down shape reconstruction. This is a serious drawback for implementing PS approaches, since most common materials have prominent specular components. In this paper, we propose a PS model that solves the problem for both diffuse and specular components aimed at shape recovery of generic objects with the approach being independent of the albedo values thanks to the image ratio formulation used. Notably, we show that by including specularities, it is possible to solve the PS problem for a minimal number of three images using a setup with three calibrated lights and a standard industrial camera. Even if an initial separation of diffuse and specular components is still required for each input image, experimental results on synthetic and real objects demonstrate the feasibility of our approach for shape reconstruction of complex geometries.The first author acknowledges the support of INDAM under the GNCS research Project “Metodi numerici per la regolarizzazione nella ricostruzione feature-preserving di dati.