Toward True 3D Object Recognition

题目：迈向真正的3D物体识别pi: Ponce， JeanU伊利诺伊大学厄巴纳-香槟分校这个提案解决了在照片和图像序列中识别三维（3D）物体的问题。它重新审视了视点不变量作为形状和外观的局部表示。关键的见解是，尽管光滑表面在大范围内几乎从不是平面的，因此（通常）不承认全局不变量，但它们在小范围内总是平面的——也就是说，足够小的表面斑块总是可以被认为是由共面点组成的——因此可以用平面不变量局部表示。这是一种新的、统一的对象识别方法的基础，其中对象模型由一组小（平面）补丁、它们的不变量和它们的3D空间关系的描述组成。具体来说，本提议中使用的局部不变量是Lindeberg和Garding以及Mikolajczyk和Schmid最近提出的对感兴趣点附近图像亮度模式的仿射不变量描述。这些仿射不变补丁提供了局部物体外观的规范化表示，在视点和光照变化下不变，可以用作图像，部分或物体相似性的局部度量。利用局部不变量之间的空间关系来表示全局目标结构，驱动识别过程。提出的方法应用于三维物体识别问题的四个基本实例：(1)从一小组未注册的图像中建模刚性三维物体，并在从无约束视点拍摄的杂乱照片中识别它们；(2)非刚性变换下非均匀纹理模式的表示与识别；(3)建模和识别图像序列中的铰接物体，并应用于识别视频片段中描绘同一场景的镜头（镜头匹配）；(4)学习和识别照片和视频片段中对象类的基于部分的描述。知识价值：该项目的主要科学贡献将是(a)一个统一的3D物体识别框架，结合几何和基于外观的识别方法的优势；(b)四个目标领域的目标识别技术的基本进展，包括广泛开放的类别级识别问题；(c)一些实际应用的有效算法，包括视频分析中的镜头匹配。将为项目中解决的每个问题收集大型的、具有代表性的数据集。它们将用于系统地评估课程中开发的算法，并在万维网上提供给计算机视觉社区。更广泛的影响：随着图像来源的不断扩展，某种形式的自动对象识别技术最终必须成为每个信息系统的组成部分。然而，今天的识别系统在很大程度上仍然无法处理典型图像中常见物体的异常广泛的外观，并且在3D物体识别实现其作为监视和安全，图像检索和数据挖掘以及视频分析和注释等领域的关键使能技术的潜力之前，需要根本性的进步。在这个项目中进行的研究将是朝着这个方向的踏脚石。

Robotics and Human Augmentation ProgramABSTRACTProposal #: 308087Title: Toward True 3D Object RecognitionPI: Ponce, JeanU of Ill Urbana-ChampaignThis proposal addresses the problem of recognizing three-dimensional (3D) objects in photographs and image sequences. It revisits viewpoint invariants as a local representation of shape and appearance. The key insight is that, although smooth surfaces are almost never planar in the large, and thus do not (in general) admit global invariants, they are always planar in the small---that is, sufficiently small surface patches can always be thought of as being comprised of coplanar points---and thus can be represented locally by planar invariants. This is the basis for a new, unified approach to object recognition where object models consist of a collection of small (planar) patches, their invariants, and a description of their 3D spatial relationship. Specifically, the local invariants used in this proposal are the affine-invariant descriptions of the image brightness pattern in the neighborhood of interest points recently developed by Lindeberg and Garding and by Mikolajczyk and Schmid. These affine-invariant patches provide a normalized representation of the local object appearance, invariant under viewpoint and illumination changes, that can be used as a local measure of image, part, or object similarity. The spatial relationship between local invariants is used to represent the global object structure and drive the recognition process. The proposed approach is applied to four fundamental instances of the 3D object recognition problem: (1) modeling rigid 3D objects from a small set of unregistered pictures and recognizing them in cluttered photographs taken from unconstrained viewpoints; (2) representing and recognizing non-uniform texture patterns under non-rigid transformations; (3) modeling and recognizing articulated objects in image sequences, with applications to the identification of shots that depict the same scene (shot matching) in video clips; and (4) learning and recognizing part-based descriptions of object classes in photographs and video clips.Intellectual Merit: The main scientific contributions of the proposed project will be (a) a unified framework for 3D object recognition that combines the advantages of geometric and appearance-based approaches to recognition; (b) fundamental advances in object recognition technology in the four target domains, including the wide open problem of category-level recognition; (c) effective algorithms for a number of practical applications, including shot matching in video analysis. Large, representative datasets will be gathered for each of the problems addressed in the project. They will be used to systematically evaluate the algorithms developed in its course, and be made available to the computer vision community at large on the World Wide Web.Broader Impacts: With the ever expanding array of imagery sources, some form of automatic object recognition technology must eventually be an integral part of every information system. However, today's recognition systems are still largely unable to handle the extraordinarily wide range of appearances assumed by common objects in typical images, and fundamental advances are needed before 3D object recognition fulfills its potential as a critical enabling technology in domains such as surveillance and security, image retrieval and data mining, and video analysis and annotation. The research conducted in this project will be a stepping stone in that direction.