The consistent alignment of point clouds obtained from multiple scanning positions is a crucial step for many 3D modeling systems. This is especially true for environment modeling. In order to observe the full scene, a common approach is to rotate the scanning device around a rotation axis using a turntable. The final alignment of each frame data can be computed from the position and orientation of the rotation axis. However, in practice, the precise mounting of scanning devices is impossible. It is hard to locate the vertical support of the turntable and rotation axis on a common line, particularly for lower cost consumer hardware. Therefore the calibration of the rotation axis of the turntable is an important step for the 3D reconstruction. In this paper we propose a novel calibration method for the rotation axis of the turntable. With the proposed rotation axis calibration method, multiple 3D profiles of the target scene can be aligned precisely. In the experiments, three different evaluation approaches are used to evaluate the calibration accuracy of the rotation axis. The experimental results show that the proposed rotation axis calibration method can achieve a high accuracy.

We propose a non-blind deconvolution algorithm of point cloud attributes inspired by multi-Wiener SURE-LET deconvolution for images. The image reconstructed by the SURE-LET approach is expressed as a linear combination of multiple filtered images where the filters are defined on the frequency domain. The coefficients of the linear combination are calculated so that the estimate of mean squared error between the original and restored images is minimized. Although the approach is very effective, it is only applicable to images. Recently we have to handle signals on irregular grids, e.g., texture data on 3D models, which are often blurred due to diffusion or motions of objects. However, we cannot utilize image processing-based approaches straightforwardly since these high-dimensional signals cannot be transformed into their frequency domain. To overcome the problem, we use graph signal processing (GSP) for deblurring the complex-structured data. That is, the SURE-LET approach is redefined on GSP, where the Wiener-like filtering is followed by the subband decomposition with an analysis graph filter bank, and then thresholding for each subband is performed. In the experiments, the proposed method is applied to blurred textures on 3D models and synthetic sparse data. The experimental results show clearly deblurred signals with SNR improvements.

This paper presents new results in the field of very low bitrate coding and compression using 3D informations. Contrary to prior art in model-based coding where 3D models have to be known, the 3D models are automatically computed from the original video sequence. The camera parameters and the scene content are supposed unknown and the video sequence is processed on the fly. A stream of 3D models is then extracted and compressed, using adapted compression techniques. We finally show the results of the proposed compression scheme, and the efficiency of this approach.

In the past decade, significant effort has been made toward increasing the accuracy and robustness of three-dimensional scanning methods. In this paper, we present a new prototype vision system named 3D Model Studio, which has been built to reconstruct a complete 3D model in as less as a few minutes. New schemes for a probe calibration and a 3D data merging (axis consolidation) are employed. We also propose a new semi-automatic contour registration method to generate accurate contour model from 3D data points, along with a contour triangulation based surface reconstruction. Experimental result shows that our system works well for reconstructing a complete 3D surface model of a human body.

Files of 3D mesh models are often large and hence time-consuming to retrieve from a storage device or to download through the network. Most 3D viewing applications need to obtain the entire file of a 3D model in order to display the model, even when the user is interested only in a small part, or a low-resolution version, of the model. Therefore, coding that enables multiresolution and ROI (Region Of Interest) transmission of 3D models is desired. In this paper, we propose a coding algorithm of 3D models based on partitioning schemes. The algorithm actually partitions the 3D meshes into some small sub-meshes according to some geometric criteria (such as curvatures), and then codes each small sub-meshes separately to transmit it progressively to users on demand. The key idea of this paper lies in the mesh partitioning procedure prior to its LOD control, which enables good compression ratio of the mesh data as well as some other good capable properties through network transmission such as ROI coding, view-adaptive transmission, error resilient coding, etc.

Due to the rapid development of computer and information technology, 3D modeling and rendering capabilities are becoming increasingly important in many applications, including industrial design, architecture, CAD/CAM, video games, and medical imaging. Since 3D mesh models often have huge amounts of the data, it is time-consuming to retrieve from a storage device or to download from the network. Most 3D viewing applications need to obtain the entire file of a 3D model in order to display the model, even when the user is interested only in a low-resolution version of the model. Therefore, progressive coding that enables multiresolution transmission of 3D models is desired. In this paper, we propose the progressive coding scheme of 3D meshes with texture, in which we convert irregular meshes to semi-regular using texture coordinates, map them on planes, and apply 2D image coding algorithm to mesh compression. As our method uses the wavelet transform, the encoded bitstream has a progressive nature. We gain high compression rate with the same visual quality as original models.

In the field of computer vision and computer graphics, Image-Based-Rendering (IBR) methods are often used to synthesize images from real scene. The image synthesis by IBR requires dense correct matching points in the images. However, IBR does not require 3D geometry reconstruction or camera calibration in Euclidean geometry. On the other hand, 3D reconstructed model can easily point out the occlusion in images. In this paper, we propose an approach to reconstruct 3D shape in a voxel space, which is named Projective Voxel Space (PVS). Since PVS is defined by projective geometry, it requires only weak calibration. PVS is determined by rectifications of the epipolar lines in three images. Three rectified images are orthogonal projected images of a scene in PVS, so processing about image projection is easy in PVS. In both PVS and Euclidean geometry, a point in an image is on a projection from a point on a surface of the object in the scene. Then the other image might have a correct matching point without occlusion, or no matching point because of occlusion. This is a kind of restriction about searching matching points or surface of the object. Taking advantage of simplicity of projection in PVS, the correlation values of points in images are computed, and the values are iteratively refined using the restriction described above. Finally, the shapes of the objects in the scene are acquired in PVS. The reconstructed shape in PVS does not have similarity to 3D shape in Euclidean geometry. However, it denotes consistent matching points in three images, and also indicates the existence of occluded points. Therefore, the reconstructed shape in PVS is sufficient for image synthesis by IBR.

In this paper, a method of fusing ground-based laser range image and CCD images for the reconstruction of textured 3D urban object is proposed. An acquisition system is developed to capture laser range image and CCD images simultaneously from the same platform. A registration method is developed using both laser range and CCD images in a coarse-to-fine process. Laser range images are registered with an assumption on sensor's setup, which aims at robustly detecting an initial configuration between the sensor's coordinate system of two views. CCD images are matched to refine the accuracy of the initial transformation, which might be degraded by improper sensor setup, unreliable feature extraction, or limited by low spatial resolution of laser range image. Textured 3D model is generated using planar faces for vertical walls and triangular cells for ground surface, trees and bushes. Through an outdoor experiment of reconstructing a building using six views of laser range and CCD images, it is demonstrated that textured 3D model of urban objects can be generated in an automated manner.