Methods of 3D Object Shape Acquisition

Sensors, 2013

This paper describes the design of a 3D image acquisition system dedicated to natural complex scenes composed of randomly distributed objects with spatial discontinuities. In agronomic sciences, the 3D acquisition of natural scene is difficult due to the complex nature of the scenes. Our system is based on the Shape from Focus technique initially used in the microscopic domain. We propose to adapt this technique to the macroscopic domain and we detail the system as well as the image processing used to perform such technique. The Shape from Focus technique is a monocular and passive 3D acquisition method that resolves the occlusion problem affecting the multi-cameras systems. Indeed, this problem occurs frequently in natural complex scenes like agronomic scenes. The depth information is obtained by acting on optical parameters and mainly the depth of field. A focus measure is applied on a 2D image stack previously acquired by the system. When this focus measure is performed, we can create the depth map of the scene.

This paper presents a complete system for scanning the geometry and surface color of a 3D object and for displaying realistic images of the object from arbitrary viewpoints. A stereo system with active light produces several views of dense range and color data. The data is registered and a surface that approximates the data is constructed. The surface estimate can be fairly coarse, as the appearance of fine detail is recreated by view-dependent texturing of the surface using color images.

Nowadays computer graphics allows to render realistic images of the 3D world. However, before such images can be generated, graphics models of the world must be available. Traditionally, these models where obtained using 3D modelling packages. This is a very time consuming process and the achievable level of detail and realism is limited. Therefore, more and more 3D models are obtained by sampling the world. Different approaches are available for this. Best known are probably laser range scanners and other devices that project light onto the object and obtain 3D information by observing the reflection. Another approach consists of using images of the scene or objects one wishes to reconstruct. A simple example consists of taking a picture to use it as a texture map for a 3D model. However, not only the appearance, but also the 3D shape can be extracted from images. This will be the main subject of this paper.

ACTA IMEKO

In this work the performances of three different techniques for 3D scanning have been investigated. In particular two commercial tools (smartphone camera and iPad Pro LiDAR) and a structured light scanner (Go!SCAN 50) have been used for the analysis. First of all, two different subjects have been scanned with the three different techniques and the obtained 3D model were analysed in order to evaluate the respective reconstruction accuracy. A case study involving a child was then considered, with the main aim of providing useful information on performances of scanning techniques for clinical applications, where boundary conditions are often challenging (i.e., non-collaborative patient). Finally, a full procedure for the 3D reconstruction of a human shape is proposed, in order to setup a helpful workflow for clinical applications.

Computer Graphics Forum, 2002

Three-dimensional (3D) image acquisition systems are rapidly becoming more affordable, especially systems based on commodity electronic cameras. At the same time, personal computers with graphics hardware capable of displaying complex 3D models are also becoming inexpensive enough to be available to a large population. As a result, there is potentially an opportunity to consider new virtual reality applications as diverse as cultural heritage and retail sales that will allow people to view realistic 3D objects on home computers. Although there are many physical techniques for acquiring 3D data-including laser scanners, structured light and time-of-flight-there is a basic pipeline of operations for taking the acquired data and producing a usable numerical model. We look at the fundamental problems of range image registration, line-of-sight errors, mesh integration, surface detail and color, and texture mapping. In the area of registration we consider both the problems of finding an initial global alignment using manual and automatic means, and refining this alignment with variations of the Iterative Closest Point methods. To account for scanner line-of-sight errors we compare several averaging approaches. In the area of mesh integration, that is finding a single mesh joining the data from all scans, we compare various methods for computing interpolating and approximating surfaces. We then look at various ways in which surface properties such as color (more properly, spectral reflectance) can be extracted from acquired imagery. Finally, we examine techniques for producing a final model representation that can be efficiently rendered using graphics hardware.