Accessing Web3D contents is relatively slow through Internet under limited bandwidth. Preprocessing of 3D models can certainly alleviate the problem, such as 3D compression and progressive meshes (PM). But none of them considers the similarity between components of a 3D model, so that we could take advantage of this to further improve the efficiency. This paper proposes a similarity-aware data reduction method together with PM, called lightweight progressive meshes (LPM). LPM aims to excavate similar components in a 3D model, generates PM representation of each component left after removing redundant components, and organizes all the processed data using a structure called lightweight scene graph. The proposed LPM possesses four significant advantages. First, it can minimize the file size of 3D model dramatically without almost any precision loss. Because of this, minimal data is delivered. Second, PM enables the delivery to be progressive, so called streaming. Third, when rendering at client side, due to lightweight scene graph, decompression is not necessary and instanced rendering is fully exerted. Fourth, it is extremely efficient and effective under very limited bandwidth, especially when delivering large 3D scenes. Performance on real data justifies the effectiveness of our LPM, which improves the state-of-the-art in accessing Web3D contents.
We present an intelligent method to automatically detect repetitive components in 3D mechanical engineering models. In our work, a new Voxel-based Shape Descriptor (VSD) is proposed for effective matching, based on which a similarity function is defined. It uses the voxels intersecting with 3D outline of mechanical components as the feature descriptor. Because each mechanical component may have different poses, the alignment before the matching is needed. For the alignment, we adopt the genetic algorithm to search for optimal solution where the maximum global similarity is the objective. Two components are the same if the maximum global similarity is over a certain threshold. Note that the voxelization of component during feature extraction and the genetic algorithm for searching maximum global similarity are entirely implemented on GPU; the efficiency is improved significantly than with CPU. Experimental results show that our method is more effective and efficient than that existing methods for repetitive components detection.
This paper presents a new method that can largely compress massive models which consist of a wide range of connected components. Its effectiveness relies mainly on the number and the complexity of repeated components being found in the input model. Comparing with the state-of-the-art algorithm of 3D model compression based on the reuse of repeated components, our method can find more repeated components, both efficiently and precisely, so that high compression ratio is achieved with no further compression of the unique components and transformation matrices. By employing reflection-invariant transformation and other optimization means during the alignment preprocessing of 3D models, we solve some problems existing in previous methods simply, like PCA ambiguities. Especially thanks to the matching scheme based on voxelization, our method itself is robust when confronting the situation that covariance matrix of PCA is degenerated. Experimental results show that our method reduces considerably and stably the number of connected components in 3D models than the state-ofthe-art algorithm so as to higher compression efficiency, and saves time around 20 times on average.
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