The goal of urban building mesh simplification is to generate a compact representation of a building from a given mesh. Local smoothness and sharp contours of urban buildings are important features for converting unstructured data into solid models, which should be preserved during the simplification. In this paper, we present a general method to filter and simplify 3D building mesh models, capable of preserving piecewise planar structures and sharp features. Given a building mesh model, a mesh filtering technique is firstly designed to yield piecewise planar regions and extract crease contours. The planar regions are used to constrain the simplification of the mesh. Mesh decimation is achieved through a series of edge collapse operations, which uses regional structural constraints and local geometric error metrics to handle planar and non-planar areas respectively. The proposed method preserves the mesh structure with meaningful levels of detail while reducing the number of faces. The effectiveness of this method is evaluated on various building models generated from different observation scales, and the performance is validated by extensive comparisons to state-of-the-art techniques.
Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM’s estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations.
We introduce a novel approach for the polygonization of Multi-view Stereo (MVS) meshes of buildings,
which results in compact and topologically valid models. The main characteristic of our method is structure
awareness, i.e., the recovery and preservation of the initial mesh primitives and their adjacencies. Our proposed
methodology consists of three main stages: (a) primitive detection via mesh segmentation, (b) encoding of
primitive adjacencies into a graph, and (c) polygonization. Polygonization is based on the approximation of
the original mesh with a candidate set of planar polygonal faces. On this candidate set, we apply a binary
labelling formulation to select and assemble an optimal set of faces under hard constraints that ensure that
the final model is both manifold and watertight. Experiments on various building models demonstrate that
our simplification method can produce simpler representations for both closed and open building meshes.
Furthermore, these representations highly conform to the initial structure and are ready to be used for
spatial analysis. The source code of this work is freely available at https://github.com/VasileiosBouzas/MeshPolygonization.
Traditional point cloud registration methods require
large overlap between scans, which imposes strict constraints on
data acquisition. To facilitate registration, users have to carefully
position scanners to ensure sufficient overlap. In this work, we
propose to use high-level structural information (i.e., plane/line
features and their inter-relationship) for registration, which is
capable of registering point clouds with small overlap, allowing
more freedom in data acquisition. We design a novel plane/linebased descriptor dedicated to establishing structure level correspondences between point clouds. Based on this descriptor,
we propose a simple but effective registration algorithm. We
also provide a dataset of real-world scenes containing a larger
number of scans with a wide range of overlap. Experiments and
comparisons with state-of-the-art methods on various datasets
reveal that our method is superior to existing techniques. Though
the proposed algorithm outperforms state-of-the-art methods
on the most challenging dataset, the point cloud registration
problem is still far from being solved, leaving significant room
for improvement and future work.
Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation,
which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling
from laser scanning data are vast. However, some works cannot guarantee sufficient modelling
accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs.
In this paper, we propose a novel method to accurately and automatically reconstruct detailed 3D
tree models from laser scans. We first extract an initial tree skeleton from the input point cloud by
establishing a minimum spanning tree using the Dijkstra shortest-path algorithm. Then, the initial tree
skeleton is pruned by iteratively removing redundant components. After that, an optimization-based
approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches.
Experiments on various types of trees from different data sources demonstrate the effectiveness and
robustness of our method. The overall fitting error (i.e., the distance between the input points and the
output model) is less than 10 cm. The reconstructed tree models can be further applied in the precise
estimation of tree attributes, urban landscape visualization, etc. The source code of this work is freely
available at https://github.com/tudelft3d/adtree
In 3D printing, it is critical to use as few as possible supporting materials for efficiency
and material saving. Multiple model decomposition methods and multi-DOF (degrees
of freedom) 3D printers have been developed to address this issue. However, most
systems utilize model decomposition and multi-DOF independently. Only a few
existing approaches combine the two, i.e. partitioning the models for multi-DOF
printing. In this paper, we present a novel model decomposition method for multidirectional 3D printing, allowing consistent printing with the least cost of supporting
materials. Our method is based on a global optimization that minimizes the surface
area to be supported for a 3D model. The printing sequence is determined inherently
by minimizing a single global objective function. Experiments on various complex
3D models using a five-DOF 3D printer have demonstrated the effectiveness of our
This paper presents a method for generative design of decorative architectural parts such as corbel,moulding and panel, which
usually have clear structure and aesthetic details. The method is composed of two components: offline learning and online
generation. The offline learning trains a 2D CurveInfoGAN and a 3D VoxelVAE that learn the feature representations of the
parts in a dataset. The online generation proceeds with an evolution procedure that evolves to product new generation of
part components by selecting, crossing over and mutating features, followed by a feature-driven deformation that synthesizes
the 3D mesh representation of new models. Built upon these technical components, a generative design tool is developed,
which allows the user to input a decorative architectural model as a reference and then generates a set of new models that
are “more of the same” as the reference and meanwhile exhibit some “surprising” elements. The experiments demonstrate
the effectiveness of the method and also showcase the use of classic geometric modelling and advanced machine learning
techniques in modelling of architectural parts.
Wenqing OuyangUniversity of Science and Technology of ChinaYue PengUniversity of Science and Technology of ChinaYuxin YaoUniversity of Science and Technology of ChinaJuyong ZhangUniversity of Science and Technology of China Bailin DengCardiff University
Geometric Modeling and Processingmathscidoc:2012.16004
Computer Graphics Forum (Symposium on Geometry Processing), 39, (5), 2020.8
The alternating direction multiplier method (ADMM) is widely used in computer graphics for solving optimization problems that can be nonsmooth and nonconvex. It converges quickly to an approximate solution, but can take a long time to converge to a solution of high‐accuracy. Previously, Anderson acceleration has been applied to ADMM, by treating it as a fixed‐point iteration for the concatenation of the dual variables and a subset of the primal variables. In this paper, we note that the equivalence between ADMM and Douglas‐Rachford splitting reveals that ADMM is in fact a fixed‐point iteration in a lower‐dimensional space. By applying Anderson acceleration to such lower‐dimensional fixed‐point iteration, we obtain a more effective approach for accelerating ADMM. We analyze the convergence of the proposed acceleration method on nonconvex problems, and verify its effectiveness on a variety of computer graphics including geometry processing and physical simulation.
The alternating direction method of multipliers (ADMM) is a popular approach for solving optimization problems that are potentially non-smooth and with hard constraints. It has been applied to various computer graphics applications, including physical simulation, geometry processing, and image processing. However, ADMM can take a long time to converge to a solution of high accuracy. Moreover, many computer graphics tasks involve non-convex optimization, and there is often no convergence guarantee for ADMM on such problems since it was originally designed for convex optimization. In this paper, we propose a method to speed up ADMM using Anderson acceleration, an established technique for accelerating fixed-point iterations. We show that in the general case, ADMM is a fixed-point iteration of the second primal variable and the dual variable, and Anderson acceleration can be directly applied. Additionally, when the problem has a separable target function and satisfies certain conditions, ADMM becomes a fixed-point iteration of only one variable, which further reduces the computational overhead of Anderson acceleration. Moreover, we analyze a particular non-convex problem structure that is common in computer graphics, and prove the convergence of ADMM on such problems under mild assumptions. We apply our acceleration technique on a variety of optimization problems in computer graphics, with notable improvement on their convergence speed.