A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
High-Quality Texture Reconstruction from Multiple Scans
IEEE Transactions on Visualization and Computer Graphics
Registering Multiview Range Data to Create 3D Computer Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registration of 3-D partial surface models using luminance and depth information
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Surface reconstruction and display from range and color data
Surface reconstruction and display from range and color data
A multi-view camera tracking for modeling of indoor environment
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Projection-Based registration using color and texture information for virtual environment generation
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
Client system for realistic broadcasting: a first prototype
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
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A novel registration method is presented for 3D point clouds, acquired from a multi-view camera, for scene reconstruction. In general, conventional registration methods require a high computational complexity, and are not robust for 3D point clouds with a low precision. To remedy these drawbacks, a projection-based registration is proposed. Firstly, depth images are refined based on temporal property by excluding 3D points with large variations, and spatial property by filling holes referring to neighboring 3D points. Secondly, 3D point clouds are projected to find correspondences and fine registration is conducted through minimizing errors. Finally, final colors are evaluated using colors of correspondences, and a 3D virtual environment is reconstructed by applying the above procedure to several views. The proposed method not only reduces computational complexity by searching for correspondences on an image plane, but also enables an effective registration even for 3D points with a low precision. The generated model can be adopted for interaction with a virtual environment as well as navigation in it.