On-the-fly Curve-skeleton Computation for 3D Shapes

The curve-skeleton of a 3D object is an abstract geometrical and topological representation of its 3D shape. It maps the spatial relation of geometrically meaningful parts to a graph structure. Each arc of this graph represents a part of the object with roughly constant diameter or thickness, and approximates its centerline. This makes the curve-skeleton suitable to describe and handle articulated objects such as characters for animation. We present an algorithm to extract such a skeleton on-the-fly, both from point clouds and polygonal meshes. The algorithm is based on a deformable model evolution that captures the object's volumetric shape. The deformable model involves multiple competing fronts which evolve inside the object in a coarse-to-fine manner. We first track these fronts' centers, and then merge and filter the resulting arcs to obtain a curve-skeleton of the object. The process inherits the robustness of the reconstruction technique, being able to cope with noisy input, intricate geometry and complex topology. It creates a natural segmentation of the object and computes a center curve for each segment while maintaining a full correspondence between the skeleton and the boundary of the object.     

Statistical Modeling of the 3D Geometry and Topology of Botanical Trees

We propose a framework for statistical modeling of the 3D geometry and topology of botanical trees. We treat botanical trees as points in a tree‐shape space equipped with a proper metric that captures the geometric and the topological differences between trees. Geodesics in the tree‐shape space correspond to the optimal sequence of deformations, i.e. bending, stretching, and topological changes, which align one tree onto another. In this way, the 3D tree modeling and synthesis problem becomes a problem of exploring the tree‐shape space either in a controlled fashion, using statistical regression, or randomly by sampling from probability distributions fitted to populations in the tree‐shape space. We show how to use this framework for (1) computing statistical summaries, e.g. the mean and modes of variations, of a population of botanical trees, (2) synthesizing random instances of botanical trees from probability distributions fitted to a population of botanical trees, and (3) modeling, interactively, 3D botanical trees using a simple sketching interface. The approach is fast and only requires as input 3D botanical tree models with a known upright orientation.     

Simple Reconstruction of Tree Branches from a Single Range Image

3D modeling of trees in real environments is a challenge in computer graphics and computer vision, since the geometric shape and topological structure of trees are more complex than conventional artificial objects. In this paper, we present a multi-process approach that is mainly performed in 2D space to faithfully construct a 3D model of the trunk and main branches of a real tree from a single range image. The range image is first segmented into patches by jump edge detection based on depth discontinuity. Coarse skeleton points and initial radii are then computed from the contour of each patch. Axis directions are estimated using cylinder fitting in the neighborhood of each coarse skeleton point. With the help of axis directions, skeleton nodes and corresponding radii are computed. Finally, these skeleton nodes are hierarchically connected, and improper radii are modified based on plant knowledge. 3D models generated from single range images of real trees demonstrate the effectiveness of our method. The main contributions of this paper are simple reconstruction by virtue of image storage order of single scan and skeleton computation based on axis directions.     

3D Skeletons: A State‐of‐the‐Art Report

Given a shape, a skeleton is a thin centered structure which jointly describes the topology and the geometry of the shape. Skeletons provide an alternative to classical boundary or volumetric representations, which is especially effective for applications where one needs to reason about, and manipulate, the structure of a shape. These skeleton properties make them powerful tools for many types of shape analysis and processing tasks. For a given shape, several skeleton types can be defined, each having its own properties, advantages, and drawbacks. Similarly, a large number of methods exist to compute a given skeleton type, each having its own requirements, advantages, and limitations. While using skeletons for two‐dimensional (2D) shapes is a relatively well covered area, developments in the skeletonization of three‐dimensional (3D) shapes make these tasks challenging for both researchers and practitioners. This survey presents an overview of 3D shape skeletonization. We start by presenting the definition and properties of various types of 3D skeletons. We propose a taxonomy of 3D skeletons which allows us to further analyze and compare them with respect to their properties. We next overview methods and techniques used to compute all described 3D skeleton types, and discuss their assumptions, advantages, and limitations. Finally, we describe several applications of 3D skeletons, which illustrate their added value for different shape analysis and processing tasks.     

Printable 3D Trees

With the growing popularity of 3D printing, different shape classes such as fibers and hair have been shown, driving research toward class‐specific solutions. Among them, 3D trees are an important class, consisting of unique structures, characteristics and botanical features. Nevertheless, trees are an especially challenging case for 3D manufacturing. They typically consist of non‐volumetric patch leaves, an extreme amount of small detail often below printable resolution and are often physically weak to be self‐sustainable. We introduce a novel 3D tree printability method which optimizes trees through a set of geometry modifications for manufacturing purposes. Our key idea is to formulate tree modifications as a minimal constrained set which accounts for the visual appearance of the model and its structural soundness. To handle non‐printable fine details, our method modifies the tree shape by gradually abstracting details of visible parts while reducing details of non‐visible parts. To guarantee structural soundness and to increase strength and stability, our algorithm incorporates a physical analysis and adjusts the tree topology and geometry accordingly while adhering to allometric rules. Our results show a variety of tree species with different complexity that are physically sound and correctly printed within reasonable time. The printed trees are correct in terms of their allometry and of high visual quality, which makes them suitable for various applications in the realm of outdoor design, modeling and manufacturing.     

Interactive Large‐Scale Procedural Forest Construction and Visualization Based on Particle Flow Simulation

Interactive visualization of large forest scenes is challenging due to the large amount of geometric detail that needs to be generated and stored, particularly in scenarios with a moving observer such as forest walkthroughs or overflights. Here, we present a new method for large‐scale procedural forest generation and visualization at interactive rates. We propose a hybrid approach by combining geometry‐based and volumetric modelling techniques with gradually transitioning level of detail (LOD). Nearer trees are constructed using an extended particle flow algorithm, in which particle trails outline the tree ramification in an inverse direction, i.e. from the leaves towards the roots. Reduced geometric representation of a tree is obtained by subsampling the trails. For distant trees, a new volumetric rendering technique in pixel‐space is introduced, which avoids geometry formation altogether and enables visualization of vast forest areas with millions of unique trees. We demonstrate that a GPU‐based implementation of the proposed method provides interactive frame rates in forest overflight scenarios, where new trees are constructed and their LOD adjusted on the fly.     

Skeleton Marching-based Parallel Vascular Geometry Reconstruction Using Implicit Functions

Fast high-precision patient-specific vascular tissue and geometric structure reconstruction is an essential task for vascular tissue engineering and computer-aided minimally invasive vascular disease diagnosis and surgery. In this paper, we present an effective vascular geometry reconstruction technique by representing a highly complicated geometric structure of a vascular system as an implicit function. By implicit geometric modelling, we are able to reduce the complexity and level of difficulty of this geometric reconstruction task and turn it into a parallel process of reconstructing a set of simple short tubular-like vascular sections, thanks to the easy-blending nature of implicit geometries on combining implicitly modelled geometric forms. The basic idea behind our technique is to consider this extremely difficult task as a process of team exploration of an unknown environment like a cave. Based on this idea, we developed a parallel vascular modelling technique, called Skeleton Marching, for fast vascular geometric reconstruction. With the proposed technique, we first extract the vascular skeleton system from a given volumetric medical image. A set of sub-regions of a volumetric image containing a vascular segment is then identified by marching along the extracted skeleton tree. A localised segmentation method is then applied to each of these sub-image blocks to extract a point cloud from the surface of the short simple blood vessel segment contained in the image block. These small point clouds are then fitted with a set of implicit surfaces in a parallel manner. A high-precision geometric vascular tree is then reconstructed by blending together these simple tubular-shaped implicit surfaces using the shape-preserving blending operations. Experimental results show the time required for reconstructing a vascular system can be greatly reduced by the proposed parallel technique.

     

基于图像的树类物体的三维重建

树类物体的三维重建一直是人们很感兴趣的一个研究内容。该文利用基于图像的建模技术，实现了一个从双视点图像重建树木三维模型的系统，提出了一个自动获取树木二维主干骨架的方案，实现了二维骨架点的对应关系求解，三维骨架点的重建、简化及树表面网格的生成。实验结果表明该文提出的方法确实可行、有效。     

Memory‐Efficient Interactive Online Reconstruction From Depth Image Streams

We describe how the pipeline for 3D online reconstruction using commodity depth and image scanning hardware can be made scalable for large spatial extents and high scanning resolutions. Our modified pipeline requires less than 10% of the memory that is required by previous approaches at similar speed and resolution. To achieve this, we avoid storing a 3D distance field and weight map during online scene reconstruction. Instead, surface samples are binned into a high‐resolution binary voxel grid. This grid is used in combination with caching and deferred processing of depth images to reconstruct the scene geometry. For pose estimation, GPU ray‐casting is performed on the binary voxel grid. A one‐to‐one comparison to level‐set ray‐casting in a distance volume indicates slightly lower pose accuracy. To enable unlimited spatial extents and store acquired samples at the appropriate level of detail, we combine a hash map with a hierarchical tree representation.     

Silhouette‐Aware Warping for Image‐Based Rendering

Image‐based rendering (IBR) techniques allow capture and display of 3D environments using photographs. Modern IBR pipelines reconstruct proxy geometry using multi‐view stereo, reproject the photographs onto the proxy and blend them to create novel views. The success of these methods depends on accurate 3D proxies, which are difficult to obtain for complex objects such as trees and cars. Large number of input images do not improve reconstruction proportionally; surface extraction is challenging even from dense range scans for scenes containing such objects. Our approach does not depend on dense accurate geometric reconstruction; instead we compensate for sparse 3D information by variational image warping. In particular, we formulate silhouette‐aware warps that preserve salient depth discontinuities. This improves the rendering of difficult foreground objects, even when deviating from view interpolation. We use a semi‐automatic step to identify depth discontinuities and extract a sparse set of depth constraints used to guide the warp. Our framework is lightweight and results in good quality IBR for previously challenging environments.     

Practical Path Guiding for Efficient Light‐Transport Simulation

We present a robust, unbiased technique for intelligent light‐path construction in path‐tracing algorithms. Inspired by existing path‐guiding algorithms, our method learns an approximate representation of the scene's spatio‐directional radiance field in an unbiased and iterative manner. To that end, we propose an adaptive spatio‐directional hybrid data structure, referred to as SD‐tree, for storing and sampling incident radiance. The SD‐tree consists of an upper part—a binary tree that partitions the 3D spatial domain of the light field—and a lower part—a quadtree that partitions the 2D directional domain. We further present a principled way to automatically budget training and rendering computations to minimize the variance of the final image. Our method does not require tuning hyperparameters, although we allow limiting the memory footprint of the SD‐tree. The aforementioned properties, its ease of implementation, and its stable performance make our method compatible with production environments. We demonstrate the merits of our method on scenes with difficult visibility, detailed geometry, and complex specular‐glossy light transport, achieving better performance than previous state‐of‐the‐art algorithms.     

ExploreMaps: Efficient construction and ubiquitous exploration of panoramic view graphs of complex 3D environments

We introduce a novel efficient technique for automatically transforming a generic renderable 3D scene into a simple graph representation named ExploreMaps, where nodes are nicely placed point of views, called probes, and arcs are smooth paths between neighboring probes. Each probe is associated with a panoramic image enriched with preferred viewing orientations, and each path with a panoramic video. Our GPU‐accelerated unattended construction pipeline distributes probes so as to guarantee coverage of the scene while accounting for perceptual criteria before finding smooth, good looking paths between neighboring probes. Images and videos are precomputed at construction time with off‐line photorealistic rendering engines, providing a convincing 3D visualization beyond the limits of current real‐time graphics techniques. At run‐time, the graph is exploited both for creating automatic scene indexes and movie previews of complex scenes and for supporting interactive exploration through a low‐DOF assisted navigation interface and the visual indexing of the scene provided by the selected viewpoints. Due to negligible CPU overhead and very limited use of GPU functionality, real‐time performance is achieved on emerging web‐based environments based on WebGL even on low‐powered mobile devices.     

Procedural Tree Modeling with Guiding Vectors

We propose guiding vectors to augment graph‐based tree synthesis, in which trees are collections of least‐cost paths in a graph. Each node has an associated guiding vector; edges parallel to the guiding vector are cheap, but edges are more expensive when their orientation differs from the guiding vector. We further propose an incremental method for assigning guiding vectors over the graph, in which a node's guiding vector is an incremental rotation of that of its parent. We present a complete procedural system for tree modeling; our use of guiding vectors enables the graph‐based method to produce high‐quality tree models resembling a variety of real‐world tree species.     

Fast Feature‐Oriented Visual Connection for Large Image Collections

Deriving the visual connectivity across large image collections is a computationally expensive task. Different from current image‐oriented match graph construction methods which build on pairwise image matching, we present a novel and scalable feature‐oriented image matching algorithm for large collections. Our method improves the match graph construction procedure in three ways. First, instead of building trees repeatedly, we put the feature points of the input image collection into a single kd‐tree and select the leaves as our anchor points. Then we construct an anchor graph from which each feature can intelligently find a small portion of related candidates to match. Finally, we design a new form of adjacency matrix for fast feature similarity measuring, and return all the matches in different photos across the whole dataset directly. Experiments show that our feature‐oriented correspondence algorithm can explore visual connectivity between images with significant improvement in speed.     

Peek‐in‐the‐Pic: Flying Through Architectural Scenes From a Single Image*

Many casually taken ‘tourist’ photographs comprise of architectural objects like houses, buildings, etc. Reconstructing such 3D scenes captured in a single photograph is a very challenging problem. We propose a novel approach to reconstruct such architectural scenes with minimal and simple user interaction, with the goal of providing 3D navigational capability to an image rather than acquiring accurate geometric detail. Our system, Peek‐in‐the‐Pic, is based on a sketch‐based geometry reconstruction paradigm. Given an image, the user simply traces out objects from it. Our system regards these as perspective line drawings, automatically completes them and reconstructs geometry from them. We make basic assumptions about the structure of traced objects and provide simple gestures for placing additional constraints. We also provide a simple sketching tool to progressively complete parts of the reconstructed buildings that are not visible in the image and cannot be automatically completed. Finally, we fill holes created in the original image when reconstructed buildings are removed from it, by automatic texture synthesis. Users can spend more time using interactive texture synthesis for further refining the image. Thus, instead of looking at flat images, a user can fly through them after some simple processing. Minimal manual work, ease of use and interactivity are the salient features of our approach.     

Reconstructing 3D Shape,Albedo and Illumination from a Single Face Image

The morphable model has been employed to efficiently describe 3D face shape and the associated albedo with a reduced set of basis vectors. The spherical harmonics (SH) model provides a compact basis to well approximate the image appearance of a Lambertian object under different illumination conditions. Recently, the SH and morphable models have been integrated for 3D face shape reconstruction. However, the reconstructed 3D shape is either inconsistent with the SH bases or obtained just from landmarks only. In this work, we propose a geometrically consistent algorithm to reconstruct the 3D face shape and the associated albedo from a single face image iteratively by combining the morphable model and the SH model. The reconstructed 3D face geometry can uniquely determine the SH bases, therefore the optimal 3D face model can be obtained by minimizing the error between the input face image and a linear combination of the associated SH bases. In this way, we are able to preserve the consistency between the 3D geometry and the SH model, thus refining the 3D shape reconstruction recursively. Furthermore, we present a novel approach to recover the illumination condition from the estimated weighting vector for the SH bases in a constrained optimization formulation independent of the 3D geometry. Experimental results show the effectiveness and accuracy of the proposed face reconstruction and illumination estimation algorithm under different face poses and multiple‐light‐source illumination conditions.     

Tree Branch Level of Detail Models for Forest Navigation

We present a level of detail (LOD) method designed for tree branches. It can be combined with methods for processing tree foliage to facilitate navigation through large virtual forests. Starting from a skeletal representation of a tree, we fit polygon meshes of various densities to the skeleton while the mesh density is adjusted according to the required visual fidelity. For distant models, these branch meshes are gradually replaced with semi‐transparent lines until the tree recedes to a few lines. Construction of these complete LOD models is guided by error metrics to ensure smooth transitions between adjacent LOD models. We then present an instancing technique for discrete LOD branch models, consisting of polygon meshes plus semi‐transparent lines. Line models with different transparencies are instanced on the GPU by merging multiple tree samples into a single model. Our technique reduces the number of draw calls in GPU and increases rendering performance. Our experiments demonstrate that large‐scale forest scenes can be rendered with excellent detail and shadows in real time.     

Floating Textures

We present a novel multi‐view, projective texture mapping technique. While previous multi‐view texturing approaches lead to blurring and ghosting artefacts if 3D geometry and/or camera calibration are imprecise, we propose a texturing algorithm that warps (“floats”) projected textures during run‐time to preserve crisp, detailed texture appearance. Our GPU implementation achieves interactive to real‐time frame rates. The method is very generally applicable and can be used in combination with many image‐based rendering methods or projective texturing applications. By using Floating Textures in conjunction with, e.g., visual hull rendering, light field rendering, or free‐viewpoint video, improved rendering results are obtained from fewer input images, less accurately calibrated cameras, and coarser 3D geometry proxies.     

融合形状特征的MRG骨架树三维检索方法

提出一种基于MRG骨架树的三维模型检索方法。根据多分辨率Reeb图(MRG)的原理,提取反映模型拓扑特征的Reeb图骨架并且映射成树形结构,分析了节点的拓扑属性。针对拓扑属性在形状特征上的表达能力不足,在节点相应区域提取离散曲率和面积比例描绘局部的形状特征。有效地结合了模型的拓扑特征和形状特征计算模型的相似度。该方法突出了模型的整体拓扑特征和形状特征,实验结果表明了该方法的高效性和鲁棒性。     

3D reconstruction from 2D images with hierarchical continuous simplices

This paper presents an effective framework for the reconstruction of volumetric data from a sequence of 2D images. The 2D images are first aligned to generate an initial 3D volume, followed by the creation of a tetrahedral domain using the Carver algorithm. The resulting tetrahedralization preserves both the geometry and topology of the original dataset. Then a solid model is reconstructed using simplex splines with fitting and faring procedures. The reconstructed heterogenous volumetric model can be quantitatively analyzed and easily visualized. Our experiments demonstrated that our approach can achieve high accuracy in the data reconstruction. The novel techniques and algorithms proposed in this paper can be applied to reconstruct a heterogeneous solid model with complex geometry and topology from other visual data.