Analysis of 3D textures, also known as relief patterns is a challenging task that requires separating repetitive surface patterns from the underlying global geometry. Existing works classify entire surfaces based on one or a few patterns by extracting ad-hoc statistical properties. Unfortunately, these methods are not suitable for objects with multiple geometric textures and perform poorly on more complex shapes. In this paper, we propose a neural network for binary segmentation to infer per-point labels based on the presence of surface relief patterns. We evaluated the proposed architecture on a high resolution point cloud dataset, surpassing the state-of-the-art, while maintaining memory and computation efficiency.

Relief patterns represent a surface characteristic that can be seen as the 3D counterpart of the texture concept in 2D images. Such characteristic is well distinct from the 3D object shape but represents a good information source to recognize the object itself. The majority of state-of-the-art techniques for 2D images rely on convolution-based filtering so, the idea of extending such techniques to the mesh manifold domain is quite intriguing as much as challenging. In this paper, we propose a novel approach based on Graph Neural Networks for 3D mesh relief pattern classification. To this end, we designed a bi-level architecture that learns on data structures computed thanks to a mesh resampling algorithm that allows us to represent local surface patches uniformly, while keeping a consistent points order. The local mesh structures are represented by SpiderPatches, that aim to capture local features of the 3D mesh surface, providing a fine-grained rich representation of the relief patterns; global structures are instead captured by MeshGraphs, whose nodes are SpiderPatches, representing the mesh at a macroscopic level. We tested our architecture using SpiderPatches and MeshGraphs on the original meshes of the SHREC’17 and SHREC’20 relief patterns track datasets, showing superior performance to that reported in the literature using a comparable experimental setting.

3D scanners generate irregularly distributed cloud of points in most of the cases. Dealing with such data, often in the form of triangular meshes, requires a pre-processing step to regularize the triangle facets shape and size. In this paper, we propose CSIOR, a novel mesh regularization technique which is capable of producing quasi-equilateral triangles, and distinguished by two novel features, namely, its intrinsic ordered aspect and its preservation of the geometric texture of the surface (relief patterns). We evidence the superiority of our technique over current methods through a series of experiments performed on a variety of geometric textured surfaces.

Extending the concept of texture to the geometry of a mesh manifold surface is an emerging topic in image processing. This concept is different from gluing images to the surface, but rather indicates the presence of relief patterns that locally change the surface geometry, showing some regular and repetitive pattern. In this paper, we propose an efficient and effective framework to address this novel task, which encompasses the convolution operation and the casting of a variety of Local Binary Pattern on the mesh manifold. Results show that our technique outperforms the existing state-of-the-art methods in the challenging task of relief patterns classification.

We present a novel statistical shape model and fitting process for the 3D Constrained Local Models (CLM), exploiting the properties of Independent Component Analysis (ICA), instead of the classic use of Principal Component Analysis (PCA), and adopting a non-Gaussian distribution of the shape prior information. Using ICA permits to exploit the real distribution of shape priors by adopting a Generalised Gaussian Distribution (GGD) model. Consequently, we derive a modified approach of the mean shift optimizer by using the Expectation-Maximization algorithms. We apply this novel method for the localization of face landmarks on 3D facial mesh models, which, to the best of our knowledge, is the first employment of the CLM variant on this kind of modality. Experiments conduced on the Bosphorus face database demonstrated that our approach outperforms state-of-the-art methods.

In this paper, we present a novel approach for fusing shape and texture local binary patterns (LBPs) on a mesh for 3D face recognition. Using a recently proposed framework, we compute LBP directly on the face mesh surface, then we construct a grid of the regions on the facial surface that can accommodate global and partial descriptions. Compared with its depth-image counterpart, our approach is distinguished by the following features: 1) inherits the intrinsic advantages of mesh surface (e.g., preservation of the full geometry); 2) does not require normalization; and 3) can accommodate partial matching. In addition, it allows early level fusion of texture and shape modalities. Through experiments conducted on the BU-3DFE and Bosphorus databases, we assess different variants of our approach with regard to facial expressions and missing data, also in comparison to the state-of-the-art solutions.

In this paper, we present and experiment a novel approach for representing texture of 3D mesh manifolds using local binary patterns (LBP). Using a recently proposed framework [37], we compute LBP directly on the mesh surface, either using geometric or photometric appearance. Compared to its depth-image counterpart, our approach is distinguished by the following features: a) inherits the intrinsic advantages of mesh surface (e.g., preservation of the full geometry); b) does not require normalization; c) can accommodate partial matching. In addition, it allows early-level fusion of the geometry and photometric texture modalities. Through experiments conducted on two application scenarios, namely, 3D texture retrieval and 3D face recognition, we assess the effectiveness of the proposed solution with respect to state of the art approaches.

In this paper, we present a novel approach for fusing shape and texture local binary patterns (LBP) for 3D face recognition. Using the framework proposed in [1], we compute LBP directly on the face mesh surface, then we construct a grid of the regions on the facial surface that can accommodate global and partial descriptions. Compared to its depth-image counterpart, our approach is distinguished by the following features: a) inherits the intrinsic advantages of mesh surface; b) does not require normalization; c) can accommodate partial matching. In addition, it allows early-level fusion of texture and shape modalities. Through experiments conducted on the BU-3DFE and Bosphorus databases, we assess different variants of our approach with regard to facial expressions and missing data.