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This paper presents a new linear framework to obtain 3D scene reconstruction and camera calibration simultaneously from uncalibrated images using scene geometry. Our strategy uses the constraints of parallelism, coplanarity, colinearity, and orthogonality. These constraints can be obtained in general man-made scenes frequently. This approach can give more stable results with fewer images and allow us to gain the results with only linear operations. In this paper, it is shown that all the geometric constraints used in the previous works performed independently up to now can be implemented easily in the proposed linear method. The study on the situations that cannot be dealt with by the previous approaches is also presented and it is shown that the proposed method being able to handle the cases is more flexible in use. The proposed method uses a stratified approach, in which affine reconstruction is performed first and then metric reconstruction. In this procedure, the additional constraints newly extracted in this paper have an important role for affine reconstruction in practical situations.

A trend in 3D mesh compression is codec design with low computational complexity which preserves the input vertex and face order. However, this added information increases the complexity. We present a fast 3D mesh compression method that compresses the redundant shared vertex information between neighboring faces using simple first-order differential coding followed by fast entropy coding with a fixed length prefix. Our algorithm is feasible for low complexity designs and maintains the order, which is now part of the MPEG-4 scalable complexity 3D mesh compression standard. The proposed algorithm is 30 times faster than MPEG-4 3D mesh coding extension.