Using genetic algorithms in computer vision : registering images to 3D surface model

This paper shows a successful application of genetic algorithms in computer vision. We aim at building photorealistic 3D models of real-world objects by adding textural information to the geometry. In this paper we focus on the 2D-3D registration problem: given a 3D geometric model of an object, and optical images of the same object, we need to find the precise alignment of the 2D images to the 3D model. We generalise the photo-consistency approach of Clarkson et al. who assume calibrated cameras, thus only the pose of the object in the world needs to be estimated. Our method extends this approach to the case of uncalibrated cameras, when both intrinsic and extrinsic camera parameters are unknown. We formulate the problem as an optimisation and use a genetic algorithm to find a solution. We use semi-synthetic data to study the effects of different parameter settings on the registration. Additionally, experimental results on real data are presented to demonstrate the efficiency of the method

Registration of an uncalibrated image pair to a 3D surface model

The following data fusion problem is considered: Given a 3D geometric model of an object and two uncalibrated images of the same object, and assuming that the object surface is textured and Lambertian, precisely register the images to the model. Solving this problem is necessary for building a geometrically accurate, photorealistic model from laser-scanned 3D data and high quality images. We generalise the photo-consistency approach by Clarkson et al. [1] to the case of uncalibrated cameras, when both intrinsic and extrinsic parameters are unknown. This gives a user the freedom of taking the pictures by a conventional digital camera, from arbitrary positions and with varying zoom. We discuss a number of possible approaches to the problem and propose a method based on manual preregistration followed by a genetic optimisation algorithm. The issues of speed and robustness are addressed. Results for real data are shown. 1

Photo-consistency based registration of an uncalibrated image pair to a 3D surface model using genetic algorithm

We consider the following data fusion problem. A 3D object with textured Lambertian surface is measured and independently photographed. A triangulated model of the object and two uncalibrated images are obtained. The goal is to precisely register the images to the model. Solving this problem is necessary for building a geometrically accurate, photorealistic model from laser-scanned 3D data and high quality images. Recently, we have proposed a novel method that generalises the photo-consistency approach by Clarkson et al. [2] to the case of uncalibrated cameras, when both intrinsic and extrinsic parameters are unknown. This gives a user the freedom of taking the pictures by a conventional digital camera, from arbitrary positions and with varying zoom. The method is based on manual pre-registration followed by a genetic optimisation algorithm. A brief description of the pilot version of the method [8] has been given together with the results of a few initial tests. In this paper, we report on some new significant developments in this project. The critical issue of robustness against illumination changes is addressed and various colour representations and cost functions are tested and compared. Natural constraints are introduced and experimentally validated to simplify the camera model and accelerate the algorithm. Finally, we present synthetic and real data with ground truth, apply the improved method to the data and measure the quality of the results. 1