PackMerger: A 3D Print Volume Optimizer

We propose an optimization framework for 3D printing that seeks to save printing time and the support material required to print 3D shapes. Three‐dimensional printing technology is rapidly maturing and may revolutionize how we manufacture objects. The total cost of printing, however, is governed by numerous factors which include not only the price of the printer but also the amount of material and time to fabricate the shape. Our PackMerger framework converts the input 3D watertight mesh into a shell by hollowing its inner parts. The shell is then divided into segments. The location of splits is controlled based on several parameters, including the size of the connection areas or volume of each segment. The pieces are then tightly packed using optimization. The optimization attempts to minimize the amount of support material and the bounding box volume of the packed segments while keeping the number of segments minimal. The final packed configuration can be printed with substantial time and material savings, while also allowing printing of objects that would not fit into the printer volume. We have tested our system on three different printers and it shows a reduction of 5–30% of the printing time while simultaneously saving 15–65% of the support material. The optimization time was approximately 1 min. Once the segments are printed, they need to be assembled.     

STL模型特征信息的自适应分层的研究

模型的分层是3D打印前处理的一个重要环节，针对目前分层算法效率低、不能有效保留模型细微特征的问题，提出了一种新的基于STL模型特征信息的思想。首先提取模型的特征边，对特征边内实体表面采用区域增长算法进行表面分割，然后对分割后的表面判断其类型，最后对于不同的表面类型采用不同的分层求交处理算法。利用VC++6.0平台和OpenGL显示技术，依据模型表面几何特征自适应地变动层厚，减小台阶效应，加快分层效率，同样能很好地保留模型局部特征，且该算法可以根据实际模型的成型方向，实现分层方向的改变，保证成型零件的精度。     

用于微流控制备的3D打印机设计

针对微流控芯片传统加工工艺成本较高,工时较长等问题提出了一种低成本、适用于微流控芯片制备的3D打印机设计方案,该设计方案由3D打印机本体和上位机控制软件组成,其中上位机控制软件负责将事先建好的三维模型进行分析、切片,并生成G-code格式文件;3D打印控制系统负责接收、解析G-code文件及转化为打印机可识别的控制指令以完成物体的快速成型。详细阐述了3D打印机各功能模块的具体实现,给出了测试打印结果,证明该打印机具有成本低、精度高的优点。     

Clever Support: Efficient Support Structure Generation for Digital Fabrication

We introduce an optimization framework for the reduction of support structures required by 3D printers based on Fused Deposition Modeling (FDM) technology. The printers need to connect overhangs with the lower parts of the object or the ground in order to print them. Since the support material needs to be printed first and discarded later, optimizing its volume can lead to material and printing time savings. We present a novel, geometry‐based approach that minimizes the support material while providing sufficient support. Using our approach, the input 3D model is first oriented into a position with minimal area that requires support. Then the points in this area that require support are detected. For these points the supporting structure is progressively built while attempting to minimize the overall length of the support structure. The resulting structure has a tree‐like shape that effectively supports the overhangs. We have tested our algorithm on the MakerBot® Replicator? 2 printer and we compared our solution to the embedded software solution in this printer and to Autodesk® Meshmixer? software. Our solution reduced printing time by an average of 29.4% (ranging from 13.9% to 49.5%) and the amount of material by 40.5% (ranging from 24.5% to 68.1%).     

Video Brush: A Novel Interface for Efficient Video Cutout

We present Video Brush, a novel interface for interactive video cutout. Inspired by the progressive selection scheme in images, our interface is designed to select video objects by painting on successive frames as the video plays. The video objects are progressively selected by solving the graph‐cut based local optimization according to the strokes drawn by the brush on each painted frame. In order to provide users interactive feedback, we accelerate 3D graph‐cut by efficient graph building and multi‐level banded graph‐cut. Experimental results show that our novel interface is both intuitive and efficient for video cutout.     

3D Motion Completion in Crowded Scenes

Crowded motions refer to multiple objects moving around and interacting such as crowds, pedestrians and etc. We capture crowded scenes using a depth scanner at video frame rates. Thus, our input is a set of depth frames which sample the scene over time. Processing such data is challenging as it is highly unorganized, with large spatio‐temporal holes due to many occlusions. As no correspondence is given, locally tracking 3D points across frames is hard due to noise and missing regions. Furthermore global segmentation and motion completion in presence of large occlusions is ambiguous and hard to predict. Our algorithm utilizes Gestalt principles of common fate and good continuity to compute motion tracking and completion respectively. Our technique does not assume any pre‐given markers or motion template priors. Our key‐idea is to reduce the motion completion problem to a 1D curve fitting and matching problem which can be solved efficiently using a global optimization scheme. We demonstrate our segmentation and completion method on a variety of synthetic and real world crowded scanned scenes.     

C‐LOD: Context‐aware Material Level‐of‐Detail applied to Mobile Graphics

Attention‐based Level‐Of–Detail (LOD) managers downgrade the quality of areas that are expected to go unnoticed by an observer to economize on computational resources. The perceptibility of lowered visual fidelity is determined by the accuracy of the attention model that assigns quality levels. Most previous attention based LOD managers do not take into account saliency provoked by context, failing to provide consistently accurate attention predictions. In this work, we extend a recent high level saliency model with four additional components yielding more accurate predictions: an object‐intrinsic factor accounting for canonical form of objects, an object‐context factor for contextual isolation of objects, a feature uniqueness term that accounts for the number of salient features in an image, and a temporal context that generates recurring fixations for objects inconsistent with the context. We conduct a perceptual experiment to acquire the weighting factors to initialize our model. We design C‐LOD, a LOD manager that maintains a constant frame rate on mobile devices by dynamically re‐adjusting material quality on secondary visual features of non‐attended objects. In a proof of concept study we establish that by incorporating C‐LOD, complex effects such as parallax occlusion mapping usually omitted in mobile devices can now be employed, without overloading GPU capability and, at the same time, conserving battery power.     

Using a Visual Attention Model to Improve Gaze Tracking Systems in Interactive 3D Applications

This paper introduces the use of a visual attention model to improve the accuracy of gaze tracking systems. Visual attention models simulate the selective attention part of the human visual system. For instance, in a bottom‐up approach, a saliency map is defined for the image and gives an attention weight to every pixel of the image as a function of its colour, edge or intensity. Our algorithm uses an uncertainty window, defined by the gaze tracker accuracy, and located around the gaze point given by the tracker. Then, using a visual attention model, it searches for the most salient points, or objects, located inside this uncertainty window, and determines a novel, and hopefully, better gaze point. This combination of a gaze tracker together with a visual attention model is considered as the main contribution of the paper. We demonstrate the promising results of our method by presenting two experiments conducted in two different contexts: (1) a free exploration of a visually rich 3D virtual environment without a specific task, and (2) a video game based on gaze tracking involving a selection task. Our approach can be used to improve real‐time gaze tracking systems in many interactive 3D applications such as video games or virtual reality applications. The use of a visual attention model can be adapted to any gaze tracker and the visual attention model can also be adapted to the application in which it is used.     

多角度融合的RGB-D显著检测

显著检测是计算机视觉的重要组成部分,但大部分的显著检测工作着重于2D图像的分析,并不能很好地应用于RGB-D图片的显著检测。受互补的显著关系在2D图像检测中取得的优越效果的启发,并考虑RGB-D图像包含的深度特征,提出多角度融合的RGB-D显著检测方法。此方法主要包括三个部分,首先,构建颜色深度特征融合的图模型,为显著计算提供准确的相似度关系;其次,利用区域的紧密度进行全局和局部融合的显著计算,得到相对准确的初步显著图;最后,利用边界连接权重和流形排序进行背景和前景融合的显著优化,得到均匀平滑的最终显著图。在RGBD1000数据集上的实验对比显示,所提出的方法超越了当前流行的方法,表明多个角度互补关系的融合能够有效提高显著检测的准确率。     

Flow Curves: an Intuitive Interface for Coherent Scene Deformation

Effective composition in visual arts relies on the principle of movement, where the viewer's eye is directed along subjective curves to a center of interest. We call these curves subjective because they may span the edges and/or center‐lines of multiple objects, as well as contain missing portions which are automatically filled by our visual system. By carefully coordinating the shape of objects in a scene, skilled artists direct the viewer's attention via strong subjective curves. While traditional 2D sketching is a natural fit for this task, current 3D tools are object‐centric and do not accommodate coherent deformation of multiple shapes into smooth flows. We address this shortcoming with a new sketch‐based interface called Flow Curves which allows coordinating deformation across multiple objects. Core components of our method include an understanding of the principle of flow, algorithms to automatically identify subjective curve elements that may span multiple disconnected objects, and a deformation representation tailored to the view‐dependent nature of scene movement. As demonstrated in our video, sketching flow curves requires significantly less time than using traditional 3D editing workflows.     

Saliency‐aware Real‐time Volumetric Fusion for Object Reconstruction

We present a real‐time approach for acquiring 3D objects with high fidelity using hand‐held consumer‐level RGB‐D scanning devices. Existing real‐time reconstruction methods typically do not take the point of interest into account, and thus might fail to produce clean reconstruction results of desired objects due to distracting objects or backgrounds. In addition, any changes in background during scanning, which can often occur in real scenarios, can easily break up the whole reconstruction process. To address these issues, we incorporate visual saliency into a traditional real‐time volumetric fusion pipeline. Salient regions detected from RGB‐D frames suggest user‐intended objects, and by understanding user intentions our approach can put more emphasis on important targets, and meanwhile, eliminate disturbance of non‐important objects. Experimental results on real‐world scans demonstrate that our system is capable of effectively acquiring geometric information of salient objects in cluttered real‐world scenes, even if the backgrounds are changing.     

Shapes In a Box: Disassembling 3D Objects for Efficient Packing and Fabrication

Modern 3D printing technologies and the upcoming mass‐customization paradigm call for efficient methods to produce and distribute arbitrarily shaped 3D objects. This paper introduces an original algorithm to split a 3D model in parts that can be efficiently packed within a box, with the objective of reassembling them after delivery. The first step consists in the creation of a hierarchy of possible parts that can be tightly packed within their minimum bounding boxes. In a second step, the hierarchy is exploited to extract the (single) segmentation whose parts can be most tightly packed. The fact that shape packing is an NP‐complete problem justifies the use of heuristics and approximated solutions whose efficacy and efficiency must be assessed. Extensive experimentation demonstrates that our algorithm produces satisfactory results for arbitrarily shaped objects while being comparable to ad hoc methods when specific shapes are considered.     

Learning 3D Scene Synthesis from Annotated RGB‐D Images

We present a data‐driven method for synthesizing 3D indoor scenes by inserting objects progressively into an initial, possibly, empty scene. Instead of relying on few hundreds of hand‐crafted 3D scenes, we take advantage of existing large‐scale annotated RGB‐D datasets, in particular, the SUN RGB‐D database consisting of 10,000+ depth images of real scenes, to form the prior knowledge for our synthesis task. Our object insertion scheme follows a co‐occurrence model and an arrangement model, both learned from the SUN dataset. The former elects a highly probable combination of object categories along with the number of instances per category while a plausible placement is defined by the latter model. Compared to previous works on probabilistic learning for object placement, we make two contributions. First, we learn various classes of higher‐order object‐object relations including symmetry, distinct orientation, and proximity from the database. These relations effectively enable considering objects in semantically formed groups rather than by individuals. Second, while our algorithm inserts objects one at a time, it attains holistic plausibility of the whole current scene while offering controllability through progressive synthesis. We conducted several user studies to compare our scene synthesis performance to results obtained by manual synthesis, state‐of‐the‐art object placement schemes, and variations of parameter settings for the arrangement model.     

A Sketch-Based User Interface for Reconstructing Architectural Drawings

We present a framework for interactive sketching that allows users to create three‐dimensional (3D) architectural models quickly and easily from a source drawing. The sketching process has four steps. (1) The user calibrates a viewing camera by specifying the origin and vanishing points of the drawing. (2) The user outlines surface polygons in the drawing. (3) A 3D reconstruction algorithm uses perceptual constraints to determine the closest visual fit for the polygon. (4) The user can then adjust aesthetic controls to produce several stylistic effects in the scene: a smooth transition between day and night rendering, a horizon knockout effect and entourage figures. The major advantage of our approach lies in the combination of perception‐based techniques, which allow us to minimize unnecessary interactions, and a hinging‐angle scheme, which shows significant improvement in numerical stability over previous optimization‐based 3D reconstruction algorithms. We also demonstrate how our reconstruction algorithm can be extended to work with perspective images, a feature unavailable in previous approaches.     

Packing Irregular Objects in 3D Space via Hybrid Optimization

Packing problems arise in a wide variety of practical applications. The basic problem is that of placing as many objects as possible in a non‐overlapping configuration within a given container. Problems involving irregular shapes are the most challenging cases. In this paper, we consider the most general forms of irregular shape packing problems in 3D space, where both the containers and the objects can be of any shapes, and free rotations of the objects are allowed. We propose a heuristic method for efficiently packing irregular objects by combining continuous optimization and combinatorial optimization. Starting from an initial placement of an appropriate number of objects, we optimize the positions and orientations of the objects using continuous optimization. In combinatorial optimization, we further reduce the gaps between objects by swapping and replacing the deployed objects and inserting new objects. We demonstrate the efficacy of our method with experiments and comparisons.     

联合聚焦度和传播机制的光场图像显著性检测

目的 图像显著性检测方法对前景与背景颜色、纹理相似或背景杂乱的场景,存在背景难抑制、检测对象不完整、边缘模糊以及方块效应等问题。光场图像具有重聚焦能力,能提供聚焦度线索,有效区分图像前景和背景区域,从而提高显著性检测的精度。因此,提出一种基于聚焦度和传播机制的光场图像显著性检测方法。方法 使用高斯滤波器对焦堆栈图像的聚焦度信息进行衡量,确定前景图像和背景图像。利用背景图像的聚焦度信息和空间位置构建前/背景概率函数,并引导光场图像特征进行显著性检测,以提高显著图的准确率。另外,充分利用邻近超像素的空间一致性,采用基于K近邻法（K-nearest neighbor,K-NN）的图模型显著性传播机制进一步优化显著图,均匀地突出整个显著区域,从而得到更加精确的显著图。结果 在光场图像基准数据集上进行显著性检测实验,对比3种主流的传统光场图像显著性检测方法及两种深度学习方法,本文方法生成的显著图可以有效抑制背景区域,均匀地突出整个显著对象,边缘也更加清晰,更符合人眼视觉感知。查准率达到85.16%,高于对比方法,F度量（F-measure）和平均绝对误差（mean absolute error,MAE）分别为72.79%和13.49%,优于传统的光场图像显著性检测方法。结论 本文基于聚焦度和传播机制提出的光场图像显著性模型,在前/背景相似或杂乱背景的场景中可以均匀地突出显著区域,更好地抑制背景区域。     

Retargeting 3D Objects and Scenes with a General Framework

In this paper, we introduce an interactive method suitable for retargeting both 3D objects and scenes. Initially, the input object or scene is decomposed into a collection of constituent components enclosed by corresponding control bounding volumes which capture the intra‐structures of the object or semantic grouping of objects in the 3D scene. The overall retargeting is accomplished through a constrained optimization by manipulating the control bounding volumes. Without inferring the intricate dependencies between the components, we define a minimal set of constraints that maintain the spatial arrangement and connectivity between the components to regularize the valid retargeting results. The default retargeting behavior can then be easily altered by additional semantic constraints imposed by users. This strategy makes the proposed method highly flexible to process a wide variety of 3D objects and scenes under an unified framework. In addition, the proposed method achieved more general structure‐preserving pattern synthesis in both object and scene levels. We demonstrate the effectiveness of our method by applying it to several complicated 3D objects and scenes.     

特征融合与S-D概率矫正的RGB-D显著检测

目的 许多先前的显著目标检测工作都是集中在2D的图像上,并不能适用于RGB-D图像的显著性检测。本文同时提取颜色特征以及深度特征,提出了一种基于特征融合和S-D概率矫正的RGB-D显著性检测方法,使得颜色特征和深度特征相互补充。方法 首先,以RGB图像的4个边界为背景询问节点,使用特征融合的Manifold Ranking输出RGB图像的显著图;其次,依据RGB图像的显著图和深度特征计算S-D矫正概率;再次,计算深度图的显著图并依据S-D矫正概率对该显著图进行S-D概率矫正;最后,对矫正后的显著图提取前景询问节点再次使用特征融合的Manifold Ranking方法进行显著优化,得到最终的显著图。结果 利用本文RGB-D显著性检测方法对RGBD数据集上的1 000幅图像进行了显著性检测,并与6种不同的方法进行对比,本文方法的显著性检测结果更接近人工标定结果。Precision-Recall曲线（PR曲线）显示在相同召回率下本文方法的准确率较其中5种方法高,且处理单幅图像的时间为2.150 s,与其他算法相比也有一定优势。结论 本文方法能较准确地对RGB-D图像进行显著性检测。