In Structure-from-Motion (SfM) applications, the capability of integrating new visual information into existing 3D models is an important need. In particular, video streams could bring significant advantages, since they provide dense and redundant information, even if normally only relative to a limited portion of the scene. In this work we propose a fast technique to reliably integrate local but dense information from videos into existing global but sparse 3D models. We show how to extract from the video data local 3D information that can be easily processed allowing incremental growing, refinement, and update of the existing 3D models. The proposed technique has been tested against two state-of-the-art SfM algorithms, showing significant improvements in terms of computational time and final point cloud density.

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Abstract-Our recently proposed wavelet-based L-infiniteconstrained coding approach for meshes ensures that the maximum error between the vertex positions in the original and decoded meshes is guaranteed to be lower than a given upper bound. Instantiations of both L-2 and L-infinite coding approaches are demonstrated for MESHGRID, which is a scalable 3D object encoding system, part of MPEG-4 AFX. In this survey paper, we compare the novel L-infinite distortion estimator against the L-2 distortion estimator which is typically employed in 3D mesh coding systems. In addition, we show that, under certain conditions, the L-infinite estimator can be exploited to approximate the Hausdorff distance in real-time implementations.Index Terms -Distortion metric, L-infinite, L-2, MAXAD, MSE, Hausdorff, 3D mesh coding I. INTRODUCTION The diversification of content and the increasing demand in mobility has led to a proliferation of heterogeneous terminals, with diverse capabilities. Efficient storage and transmission of digital data is therefore a critical problem, which can be solved by compressing the original data based on some predefined criteria.There is a broad range of applications (e.g. in the medical area), where compact coding cannot come at the expense of information loss. A viable solution in this case is given by lossless coding, possibly coupled with multi-functionality support, such as scalability and progressive (lossy-to-lossless) reconstruction of the input data. Lossless coding is downsized however by the fairly low achievable lossless compression ratios. There are other applications, such as those in the field of remote sensing, where one can accept information loss in favor of higher compression ratios, provided that the distortions incurred are rigorously bounded. In such applications, lossy or near-lossless compression are suitable, but an appropriate distortion measure needs to be employed in order to accurately quantify and control the distortion incurred by the compression system.The ideal distortion metric in lossy coding of meshes is the Hausdorff distance, as this metric expresses the maximum local error between the original and decoded meshes. However, to compute the Hausdorff distance, considerable processing power and memory space are needed, in particular for high resolution meshes. This becomes even more critical in scalable mesh coding systems, where, in order allocate rate, one needs to determine the Hausdorff distance for all possible

Abstract-We present an innovative system to encode and transmit textured multi-resolution 3D meshes in a progressive way, with no need to send several texture images, one for each mesh LOD (Level Of Detail). All texture LODs are created from the finest one (associated to the finest mesh), but can be reconstructed progressively from the coarsest thanks to refinement images calculated in the encoding process, and transmitted only if needed. This allows us to adjust the LOD/quality of both 3D mesh and texture according to the rendering power of the device that will display them, and to the network capacity. Additionally, we achieve big savings in data transmission by avoiding altogether texture coordinates, which are generated automatically thanks to an unwrapping system agreed upon by both encoder and decoder.