MOTIVATION: Popular shape-based alignment methods handle molecular flexibility by utilizing conformational ensembles to select the most fitted conformer. However, the initial conformer library generation step is computationally intensive and limiting to the overall alignment process. In this work, we describe a method to perform flexible alignment of two molecular shapes by optimizing the 3D conformation. SENSAAS-Flex, an add-on to the SENSAAS tool, is able to proceed from a limited set of initial conformers through an iterative process where additional conformational optimizations are made at the substructure level and constrained by the target shape. RESULTS: In self- and cross-alignment experiments, SENSAAS-Flex is able to reproduce the crystal structure geometry of ligands of the AstraZeneca Molecule Overlay Test set and PDBbind refined dataset. Our study shows that the point-based representation of molecular surfaces is appropriate in terms of shape constraint to sample the conformational space and perform flexible molecular alignments. AVAILABILITY AND IMPLEMENTATION: The documentation and source code are available at https://chemoinfo.ipmc.cnrs.fr/Sensaas-flex/sensaas-flex-main.tar.gz.

We present an original workflow for structuring a point cloud generated from several scans. Our representation is based on a set of local graphs. Each graph is constructed from the depth map provided by each scan. The graphs are then connected together via the overlapping areas, and careful consideration of the redundant points in these regions leads to a piecewise and globally consistent structure for the underlying surface sampled by the point cloud. The proposed workflow allows structuring aggregated point clouds, scan after scan, whatever the number of acquisitions and the number of points per acquisition, even on computers with very limited memory capacities. To show that our structure can be highly relevant for the community, where the gigantic amount of data represents a real scientific challenge per se, we present an algorithm based on this structure capable of resampling billions of points on standard computers. This application is particularly attractive for simplifying and visualizing gigantic point clouds representing very large-scale scenes (buildings, urban scenes, historical sites...), which often require a prohibitive number of points to describe them accurately.

This paper proposes a novel prediction tool for improving the compression performance of texture atlases. This algorithm, called Geometry-Aware (GA) intra coding, takes advantage of the topology of the associated 3D meshes, in order to reduce the redundancies in the texture map. For texture processing, the concept of the conventional intra prediction, used in video compression, has been adapted to consider neighboring information on the 3D surface. We have also studied how this prediction tool can be integrated into a complete coding solution. In particular, a block scanning strategy and a graph-based transform for residual coding have been proposed. Results show that the knowledge of the mesh topology significantly improves the compression efficiency of texture atlases.

A 3D mesh object is usually represented as a combination of several entities including geometrical information (i.e., the triangles and their position in space) and a texture atlas/map (i.e. a giant 2D image containing all the texture information that is mapped to the 3D object at the rendering stage). This atlas is usually compressed using a conventional 2D image coder, thus without taking into account the geometrical information. Moreover, the whole image is usually decoded even though only a subpart of the mesh is observed by a user. In this paper, we propose a novel approach to compress a texture atlas of a 3D model that enables random access during decoding, and nevertheless takes into account the correlation driven by the geometrical information. The experimental results demonstrate the benefits of the proposed coder.

We propose a new compression method devoted to large structured hexahedral meshes having discontinuities. It is dedicated to applications such as visualization or physical simulations whose management by any workstation or mobile device with limited memory and bandwidth is critical. Our method relies on a multiresolution analysis that generates a hierarchy of meshes at increasing resolutions. Our technique also uses a discontinuity tracking feature for their preservation, whatever the resolution, and consequently maintains a coherent geometry with respect to the original mesh. Experimental results emphasize the quality of our compression, in terms of both geometrical distortion and compression ratio.

Abstract Semi‐regular triangle remeshing algorithms convert irregular surface meshes into semi‐regular ones. Especially in the field of computer graphics, semi‐regularity is an interesting property because it makes meshes highly suitable for multi‐resolution analysis. In this paper, we survey the numerous remeshing algorithms that have been developed over the past two decades. We propose different classifications to give new and comprehensible insights into both existing methods and issues. We describe how considerable obstacles have already been overcome, and discuss promising perspectives.

We propose in this paper a robust simplification technique, which preserves geometric features such as sharp edges or corners from original surfaces. To achieve this goal, our simplification process relies on a detection tool that enables to preserve the sharp features during the three subsequent steps: a Poisson disk sampling that intelligently reduces the number of vertices of the initial mesh; the meshing of the samples that aligns the edges along the feature lines; and a constrained relaxation step that improves the shape of the triangles of our final simplified mesh. Experimental results show that our method always produces valid meshes without aliasing artifacts, and without giving up the shape fidelity and quality of the mesh elements.

Our objective is to include in stereoscopic 3D acquisition systems new technologies to automatically detect deformations on aircraft fuselages. We propose in this paper a semiregular mesh reconstruction dedicated to stereoscopic scanners, combined to a multiresolution analysis tool that detects dents on smooth surfaces. The proposed technique for reconstruction is based on a coarse-to-fine approach, and creates semiregular meshes directly from the stereoscopic images. The output of our scanner is thus a structured mesh, by the way well suited for many applications unlike most of scanners that generate only point clouds. Local distances are then calculated between this semiregular mesh and a smooth version of it, in order to automatically detect dents on the scanned surface. The smooth version is obtained via a technique based on multiresolution analysis. Experimental results show the reliability of our contributions on scanned aircraft fuselages.

In this paper, we propose an optimization of the lifted Butterfly scheme for semi-regular meshes. This optimization consists in adapting prediction and update steps at each level of resolution for a given semi-regular mesh. The motivation is the improvement of the multiresolution analysis in order to increase the compression performances of the subsequent geometry coder. We first compute an optimized prediction filter that minimizes the L1-norm of the wavelet coefficients for each level of resolution, independently. We then compute the update filter in order to preserve the data average (0thmoment) at the lower resolution. Experimental results show that our technique globally reduces the entropy of the wavelet coefficients of any semi-regular mesh. Consequently our contribution also improves the compression performances of the zerotree coder PGC.

We propose an adaptive semi-regular remeshing algorithm for surface meshes. Our algorithm uses Voronoi tessellations during both simplification and refinement stages. During simplification, the algorithm constructs a first centroidal Voronoi tessellation of the vertices of the input mesh. The sites of the Voronoi cells are the vertices of the base mesh of the semi-regular output. During refinement, the new vertices added at each resolution level by regular subdivision are considered as new Voronoi sites. We then use the Lloyd relaxation algorithm to update their position, and finally we obtain uniform semi-regular meshes. Our algorithm also enables adaptive remeshing by tuning a threshold based on the mass probability of the Voronoi sites added by subdivision. Experimentation shows that our technique produces semi-regular meshes of high quality, with significantly less triangles than state of the art techniques.

Our proposal is a method for computing the distance between two surfaces modeled by massive triangle meshes which can not be both loaded entirely in memory. The method consists in loading at each step a small part of the two meshes and computing the symmetrical distance for these areas. These areas are chosen in such a way as the orthogonal projection, used to compute this distance, have to be in it. For this, one of the two meshes is simplified and then a correspondence between the simplified mesh and the triangles of the input meshes is done. The experiments show that the proposed method is very efficient in terms of memory cost, while producing results comparable to the existent tools for the small and medium size meshes. Moreover, the proposed method enables us to compute the distance for massive meshes.

A view-dependent geometry coding of 3D scenes defined by sets of semi-regular meshes is presented. The objective is to reduce the quantity of significant data to store when visualizing static 3D scenes from a specific point of view. The proposed coding scheme combines a segmentation for determining the visible regions, and an allocation process for improving the visual quality of the encoded scene.

In this paper, we present a new framework to analyse and process 3D animations (defined by sequences of triangular meshes sharing the same connectivity at any frame). Our idea is to develop a spatio-temporal wavelet filtering for such data, leading to a relevant multiresolution decomposition. In geometry processing, the most efficient spatial wavelets are based on a semiregular sampling. Since the 3D animations generally have an irregular sampling, one of our contribution is a remeshing technique, transforming an animation in a sequence of semiregular meshes, which presents regularity in time but also in space. This new sampling has the advantage to improve the quality of the multiresolution decomposition in the spatial dimension. To show the contribution of such a spatio-temporal filtering in animation processing, we present some experimental results in data compression.

The MPEG-4 standard adopted a novel technology for compression of dynamic 3D meshes with constant connectivity and time-varying geometry, referred to as FAMC - frame-based animated mesh compression. In this paper, we propose two optimizations of the FAMC approach, aiming at improving the compression efficiency. The first one is based on a PCA (principal component analysis) decomposition of the motion compensation error residuals. The second improves the bi-orthogonal (4-2) wavelet coding approach supported by the standard, by introducing an optimal bit allocation procedure, combined with an adapted quantization of wavelet coefficients. Experimental results show that both optimizations lead to significant gains in compression rate (about 20-30%) at low bitrates.

Although deforming surfaces are frequently used in numerous domains (scientific applications, games...), only few works have been proposed until now for simplifying such data. However, these time-varying surfaces are generally represented as oversampled triangular meshes with a static connectivity, involving a large number of unnecessary details for some frames. Among the related works, some methods provide globally good results, but fine details appearing during the animation are not always well-preserved, because of a static geometry sampling. We propose a new simplification method for deforming surfaces based on a dynamic geometry sampling. The idea is to compute one coarse version at the first frame, and then to progressively update the coarse sampling for the subsequent frames. In order to optimally approximate each frame, vertices are added or removed following the appearance or disappearance of fine details in the frames. Our approach is fast, easy to implement, and produces good quality time-varying approximations with well-preserved fine details, at any given frame.

The objective of this paper is to propose an efficient model-based bit allocation process optimizing the performances of a wavelet coder for semiregular meshes. More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed mean square error for one specific target bitrate. In order to design a fast and low complex allocation process, we propose an approximation of the reconstructed mean square error relative to the coding of semiregular mesh geometry. This error is expressed directly from the quantization errors of each coefficient subband. For that purpose, we have to take into account the influence of the wavelet filters on the quantized coefficients. Furthermore, we propose a specific approximation for wavelet transforms based on lifting schemes. Experimentally, we show that, in comparison with a "naive" approximation (depending on the subband levels), using the proposed approximation as distortion criterion during the model-based allocation process improves the performances of a wavelet-based coder for any model, any bitrate, and any lifting scheme.

In this paper, we propose a powerful bit allocation that optimizes the quantization of the normal mesh geometry. This bit allocation aims to minimize the surface-to-surface distance [P. Cignoni et al., 1998] between the original irregular mesh and the quantized normal one, according to a target bitrate. Moreover, to provide a fast bit allocation, we approximate this surface-to-surface distance with a simple criterion depending on the wavelet coefficient distributions, and we use theoretical models. This provides a fast and low-complex model-based bit allocation yielding results better than the recent state-of-the-art methods like [A. Khodakovsky and I. Guskov, 2002].

In this paper, we propose a 3D geometry compression technique for densely sampled surface meshes. Based on a 3D multiresolution analysis (performed by a 3D Discrete wavelet transform for semiregular meshes), this scheme includes a model-based bit allocation process across the wavelet subbands and an efficient surface adapted weighted criterion for 3D wavelet coefficient coordinates. This permits to highly improve the visual quality of quantized meshes obtained by classical bit allocation based on MSE distortion. Moreover, the coefficients are encoded with an original 3D context-based bitplane arithmetic coder. The main contribution of this paper is the introduction of 3D multiresolution contexts adapted to 3D semiregular mesh geometry information.

In this paper, we propose an efficient low complexity compression scheme for densely sampled irregular 3D meshes. This scheme is based on 3D multiresolution analysis (3D discrete wavelet transform) and includes a model-based bit allocation process across the wavelet subbands. Coordinates of 3D wavelet coefficients are processed separately and statistically modeled by a generalized Gaussian distribution. This permits an efficient allocation even at a low bitrate and with a very low complexity. We introduce a predictive geometry coding of LF subbands and topology coding is made by using an original edge-based method. The main idea of our approach is the model-based bit allocation adapted to 3D wavelet coefficients and the use of EBCOT coder to efficiently encode the quantized coefficients. Experimental results show compression ratio improvement for similar reconstruction quality compared to the well-known PGC method.