基于物体间支撑语义关系的室内场景彩色深度图像分割

场景理解是智能自主机器人领域的一个重要研究方向,而图像分割是场景理解的基础.但是,不完备的训练数据集,以及真实环境中的罕见情形,会导致在图像分割时存在先验知识不完备的情况,进而影响图像分割的效果.因此,提出在彩色深度(RGB–D)图像上使用抽象的支撑语义关系来解决多样的物体形态所面对的先验知识不完备问题.在先验知识不完备情况下,针对自底向上的图像分割过程中被过度分割出的物体块,首先对物体块间的支撑语义关系进行建模并计算其支撑概率,然后构造能够度量场景总体稳定性的能量函数,最后通过Swendsen-Wang割(SWC)随机图分割算法最小化该能量函数的值,将物体块间的支撑概率转化为强支撑语义关系并完成物体块合并,实现先验知识不完备情况下的图像分割.实验结果证明,结合支撑语义关系的图像分割能够在先验知识不完备的情况下,将同一物体被过度分割的部分重新合并起来,从而提升了图像分割的准确性.     

Saliency‐Preserving Slicing Optimization for Effective 3D Printing

We present an adaptive slicing scheme for reducing the manufacturing time for 3D printing systems. Based on a new saliency‐based metric, our method optimizes the thicknesses of slicing layers to save printing time and preserve the visual quality of the printing results. We formulate the problem as a constrained ?₀ optimization and compute the slicing result via a two‐step optimization scheme. To further reduce printing time, we develop a saliency‐based segmentation scheme to partition an object into subparts and then optimize the slicing of each subpart separately. We validate our method with a large set of 3D shapes ranging from CAD models to scanned objects. Results show that our method saves printing time by 30–40% and generates 3D objects that are visually similar to the ones printed with the finest resolution possible.     

Part‐Based Mesh Segmentation: A Survey

This paper surveys mesh segmentation techniques and algorithms, with a focus on part‐based segmentation, that is, segmentation that divides a mesh (featuring a 3D object) into meaningful parts. Part‐based segmentation applies to a single object and also to a family of objects (i.e. co‐segmentation). However, we shall not address here chart‐based segmentation, though some mesh co‐segmentation methods employ such chart‐based segmentation in the initial step of their pipeline. Finally, the taxonomy proposed in this paper is new in the sense that one classifies each segmentation algorithm regarding the dimension (i.e. 1D, 2D and 3D) of the representation of object parts. The leading idea behind this survey is to identify the properties and limitations of the state‐of‐the‐art algorithms to shed light on the challenges for future work.     

An efficient and robust algorithm for 3D mesh segmentation

This paper presents an efficient and robust algorithm for 3D mesh segmentation. Segmentation is one of the main areas of 3D object modeling. Most segmentation methods decompose 3D objects into parts based on curvature analysis. Most of the existing curvature estimation algorithms are computationally costly. The proposed algorithm extracts features using Gaussian curvature and concaveness estimation to partition a 3D model into meaningful parts. More importantly, this algorithm can process highly detailed objects using an eXtended Multi-Ring (XMR) neighborhood based feature extraction. After feature extraction, we also developed a fast marching watershed-based segmentation algorithm followed by an efficient region merging scheme. Experimental results show that this segmentation algorithm is efficient and robust.     

Application of planar shape comparison to object retrieval in image databases

A similarity measure for silhouettes of 2D objects is presented, and its properties are analyzed with respect to retrieval of similar objects in image databases. To reduce influence of digitization noise as well as segmentation errors the shapes are simplified by a new process of digital curve evolution. To compute our similarity measure, we first establish the best possible correspondence of visual parts (without explicitly computing the visual parts). Then the similarity between corresponding parts is computed and summed. Experimental results show that our shape matching procedure gives an intuitive shape correspondence and is stable with respect to noise distortions.     

基于CV／CAD的三维物体几何建模

在虚拟现实等技术领域中,都涉及到由现实世界中的实际景物建立对应的计算机描述的虚拟景物的问题,为此提出了利用计算机视觉与CAD几何建模技术相结合的三维珠体建模途径,首先通过编码光栅方法获取三维物体的深度图象,并采用数学形态学的方法加以分割,然后利用代数曲面拟合手段对分割后的三维曲面片进行重建,并使用CAD几何建模工具由重建的曲面片构成物体的几何模型,该文给出了初步的实验结果,证明所提出的技术途径基本可行。     

Tracking with occlusions via graph cuts

This work presents a new method for tracking and segmenting along time-interacting objects within an image sequence. One major contribution of the paper is the formalization of the notion of visible and occluded parts. For each object, we aim at tracking these two parts. Assuming that the velocity of each object is driven by a dynamical law, predictions can be used to guide the successive estimations. Separating these predicted areas into good and bad parts with respect to the final segmentation and representing the objects with their visible and occluded parts permit handling partial and complete occlusions. To achieve this tracking, a label is assigned to each object and an energy function representing the multilabel problem is minimized via a graph cuts optimization. This energy contains terms based on image intensities which enable segmenting and regularizing the visible parts of the objects. It also includes terms dedicated to the management of the occluded and disappearing areas, which are defined on the areas of prediction of the objects. The results on several challenging sequences prove the strength of the proposed approach.     

Convexity Rule for Shape Decomposition Based on Discrete Contour Evolution

We concentrate here on decomposition of 2D objects into meaningfulparts of visual form, orvisual parts. It is a simple observation that convex parts of objects determine visual parts. However, the problem is that many significant visual parts are not convex, since a visual part may have concavities. We solve this problem by identifying convex parts at different stages of a proposed contour evolution method in which significant visual parts will become convex object parts at higher stages of the evolution. We obtain a novel rule for decomposition of 2D objects into visual parts, called the hierarchical convexity rule, which states that visual parts are enclosed by maximal convex (with respect to the object) boundary arcs at different stages of the contour evolution. This rule determines not only parts of boundary curves but directly the visual parts of objects. Moreover, the stages of the evolution hierarchy induce a hierarchical structure of the visual parts. The more advanced the stage of contour evolution, the more significant is the shape contribution of the obtained visual parts.     

A 3D shape segmentation approach for robot grasping by parts

Neuro-psychological findings have shown that human perception of objects is based on part decomposition. Most objects are made of multiple parts which are likely to be the entities actually involved in grasp affordances. Therefore, automatic object recognition and robot grasping should take advantage from 3D shape segmentation. This paper presents an approach toward planning robot grasps across similar objects by part correspondence. The novelty of the method lies in the topological decomposition of objects that enables high-level semantic grasp planning.In particular, given a 3D model of an object, the representation is initially segmented by computing its Reeb graph. Then, automatic object recognition and part annotation are performed by applying a shape retrieval algorithm. After the recognition phase, queries are accepted for planning grasps on individual parts of the object. Finally, a robot grasp planner is invoked for finding stable grasps on the selected part of the object. Grasps are evaluated according to a widely used quality measure. Experiments performed in a simulated environment on a reasonably large dataset show the potential of topological segmentation to highlight candidate parts suitable for grasping.     

Deep learning-based optimal segmentation of 3D printed product for surface quality improvement and support structure reduction

Large-sized product cannot be printed as one piece by a 3D printer because of the volume limitation of most 3D printers. Some products with the complex structure and high surface quality should also not be printed into one piece to meet requirement of the printing quality. For increasing the surface quality and reducing support structure of 3D printed models, this paper proposes a 3D model segmentation method based on deep learning. Sub-graphs are generated by pre-segmenting 3D triangular mesh models to extract printing features. A data structure is proposed to design training data sets based on the sub-graphs with printing features of the original 3D model including surface quality, support structure and normal curvature. After training a Stacked Auto-encoder using the training set, a 3D model is pre-segmented to build an application set by the sub-graph data structure. The application set is applied by the trained deep-learning system to generate hidden features. An Affinity Propagation clustering method is introduced in combining hidden features and geometric information of the application set to segment a product model into several parts. In the case study, samples of 3D models are segmented by the proposed method, and then printed using a 3D printer for validating the performance.     

3D part segmentation using simulated electrical chargedistributions

A novel approach to 3D part segmentation is presented. It is a well-known physical fact that electrical charge on the surface of a conductor tends to accumulate at a sharp convexity and vanish at a sharp concavity. Thus, object part boundaries, which are usually denoted by a sharp surface concavity, can be detected by simulating the electrical charge density over the object surface and locating surface points which exhibit local charge density minima. Beginning with single- or multiview range data of a 3D object, we simulate the charge density distribution over an object's surface which has been tessellated by a triangular mesh. We detect the deep surface concavities by tracing local charge density minima and then decompose the object into parts at these points. The charge density computation does not require an assumption on surface smoothness and uses weighted global data to produce robust local surface features for part segmentation     

Shape similarity measure based on correspondence of visual parts

A cognitively motivated similarity measure is presented and its properties are analyzed with respect to retrieval of similar objects in image databases of silhouettes of 2D objects. To reduce influence of digitization noise, as well as segmentation errors, the shapes are simplified by a novel process of digital curve evolution. To compute our similarity measure, we first establish the best possible correspondence of visual parts (without explicitly computing the visual parts). Then, the similarity between corresponding parts is computed and aggregated. We applied our similarity measure to shape matching of object contours in various image databases and compared it to well-known approaches in the literature. The experimental results justify that our shape matching procedure gives an intuitive shape correspondence and is stable with respect to noise distortions.     

An integrated approach for range image segmentation and representation

This paper proposes an efficient method for the segmentation and representation of 3D rigid, solid objects from its range images using differential invariants derived from classical differential geometry. An efficient algorithm for derivation of surface curvatures, which are affine invariants, at smooth surface patches is proposed. The surface is approximated by Bezier and Beta-splines to compare qualitatively the proposed segmentation scheme. This scheme leads to derivation of surface features, which provides a very robust surface segmentation. An integrated approach represents the surface in terms of plane, quadric and superquadric surface.Experiments show excellent performance and together with the inherent parallelism make the scheme a promising one. Present experiments were conducted on some real range images where most of the parts of the object are planar.     

Hierarchical part-type segmentation using voxel-based curve skeletons

We present an effective framework for segmenting 3D shapes into meaningful components using the curve skeleton. Our algorithm identifies a number of critical points on the efficiently computed curve skeleton, either fully automatically as the junctions of the curve skeleton, or based on user input. We use these points to construct a partitioning of the object surface using geodesics. Because the segmentation is based on the curve skeleton, it intrinsically reflects the shape symmetry and articulation, and can handle shapes with tunnels. We describe a voxel-based implementation of our method which is robust and noise resistant, able to handle shapes of complex articulation and topology, produces smooth segment borders, and delivers hierarchical level-of-detail segmentations. We demonstrate the framework on various real-world 3D shapes. Additionally, we discuss the use of both curve and surface skeletons to produce part-type and patch-type, respectively, segmentations of 3D shapes.     

圆锥裁剪BLOB的视频目标检测方法

真实场景的视频目标检测需要消除阴影、反射和鬼影等噪声的影响,以检测出运动目标和静止目标.为了实现系统性的视频目标检测,提出一种自适应圆锥裁剪联通块(TC-BLOB)榆测方法.基于BLOB知识,将3D颜色空间变换为"夹角-模差"2D空间后,定义一套圆锥裁剪规则划分出阴影BLOB和反射BLOB;再以一种持久化记忆PM方法判别出鬼影BLOB;最后改进双背景模型检测出静止目标和运动目标.采用不同环境视频进行实验的结果表明,文中方法足有效的,并有独立于后续跟踪的优点.     

Improved Surface Quality in 3D Printing by Optimizing the Printing Direction

We present a pipeline of algorithms that decomposes a given polygon model into parts such that each part can be 3D printed with high (outer) surface quality. For this we exploit the fact that most 3D printing technologies have an anisotropic resolution and hence the surface smoothness varies significantly with the orientation of the surface. Our pipeline starts by segmenting the input surface into patches such that their normals can be aligned perpendicularly to the printing direction. A 3D Voronoi diagram is computed such that the intersections of the Voronoi cells with the surface approximate these surface patches. The intersections of the Voronoi cells with the input model's volume then provide an initial decomposition. We further present an algorithm to compute an assembly order for the parts and generate connectors between them. A post processing step further optimizes the seams between segments to improve the visual quality. We run our pipeline on a wide range of 3D models and experimentally evaluate the obtained improvements in terms of numerical, visual, and haptic quality.