In this paper, we describe a novel procedural modeling technique for generating realistic plant models from multi-view photographs. The realism is enhanced via visual and spatial information acquired from images. In contrast to previous approaches that heavily rely on user interaction to segment plants or recover branches in images, our method automatically estimates an accurate depth map of each image and extracts a 3D dense point cloud by exploiting an efficient stereophotogrammetry approach. Taking this point cloud as a soft constraint, we fit a parametric plant representation to simulate the plant growth progress. In this way, we are able to combine real data (photos and 3D point clouds) analysis with rule-based procedural plant modeling. We demonstrate the robustness of the proposed approach by modeling a variety of plants with complex branching structures and significant self-occlusions. We also demonstrate that the proposed framework can be used to reconstruct ground-covering plants, such as bushes and shrubs which have gained little attention in the literature. The effectiveness of our approach is validated by visually and quantitatively comparing with the state-of-the-art approaches.

Textureless objects, repetitive patterns and limited computational resources pose significant challenges to man-made structure reconstruction from images, because feature-points-based reconstruction methods usually fail due to the lack of distinct texture or ambiguous point matches. Meanwhile multi-view stereo approaches also suffer from high computational complexity. In this paper, we present a new framework to reconstruct 3D surfaces for buildings from multi-view images by leveraging another fundamental geometric primitive: line segments. To this end, we first propose a new multi-resolution line segment detector to extract 2D line segments from each image. Then, we construct a 3D line cloud by introducing an improved Line3D++ algorithm to match 2D line segments from different images. Finally, we reconstruct a complete and manifold surface mesh from 3D line segments by formulating a Bayesian probabilistic modeling problem , which accurately generates a set of underlying planes. This output model is simple and has low performance requirements for hardware devices. Experimental results demonstrate the validity of the proposed approach and its ability to generate abstract and compact surface meshes from the 3D line cloud with low computational costs.

In this paper, we examine the robustness of scagnostics through a series of theoretical and empirical studies. First, we investigate the sensitivity of scagnostics by employing perturbing operations on more than 60M synthetic and real-world scatterplots. We found that two scagnostic measures, Outlying and Clumpy, are overly sensitive to data binning. To understand how these measures align with human judgments of visual features, we conducted a study with 24 participants, which reveals that i) humans are not sensitive to small perturbations of the data that cause large changes in both measures, and ii) the perception of clumpiness heavily depends on per-cluster topologies and structures. Motivated by these results, we propose Robust Scagnostics (RScag) by combining adaptive binning with a hierarchy-based form of scagnostics. An analysis shows that RScag improves on the robustness of original scagnostics, aligns better with human judgments, and is equally fast as the traditional scagnostic measures.

A discriminative local shape descriptor plays an important role in various applications. In this paper, we present a novel deep learning framework that derives discriminative local descriptors for deformable 3D shapes. We use local “geometry images” to encode the multi-scale local features of a point, via an intrinsic parameterization method based on geodesic polar coordinates. This new parameterization provides robust geometry images even for badly-shaped triangular meshes. Then a triplet network with shared architecture and parameters is used to perform deep metric learning; its aim is to distinguish between similar and dissimilar pairs of points. Additionally, a newly designed triplet loss function is minimized for improved, accurate training of the triplet network. To solve the dense correspondence problem, an efficient sampling approach is utilized to achieve a good compromise between training performance and descriptor quality. During testing, given a geometry image of a point of interest, our network outputs a discriminative local descriptor for it. Extensive testing of non-rigid dense shape matching on a variety of benchmarks demonstrates the superiority of the proposed descriptors over the state-of-the-art alternatives.

The aspect ratio of a line chart heavily influences the perception of the underlying data. Different methods explore different criteria in choosing aspect ratios, but so far, it was still unclear how to select aspect ratios appropriately for any given data. This paper provides a guideline for the user to choose aspect ratios for any input 1D curves by conducting an in-depth analysis of aspect ratio selection methods both theoretically and experimentally. By formulating several existing methods as line integrals, we explain their parameterization invariance. Moreover, we derive a new and improved aspect ratio selection method, namely the $L_{1}$-LOR (local orientation resolution), with a certain degree of parameterization invariance. Furthermore, we connect different methods, including AL (arc length based method), the banking to 45° principle, RV (resultant vector) and AS (average absolute slope), as well as $L_{1}$-LOR and AO (average absolute orientation). We verify these connections by a comparative evaluation involving various data sets, and show that the selections by RV and $L_{1}$-LOR are complementary to each other for most data. Accordingly, we propose the dual-scale banking technique that combines the strengths of RV and $L_{1}$-LOR, and demonstrate its practicability using multiple real-world data sets.