Active geometric reconstruction methods for objects: a survey

目的 目标建模是机器视觉领域的主要研究方向之一,主动目标建模是在保证建模完整度的情况下,通过有计划地调节相机的位姿参数,以更少的视点和更短的运动路径实现目标建模的智能感知方法。为了反映主动目标建模的研究现状和最新进展,梳理分析了2004年以来的相关文献,对国内外研究方法做出概括性总结。方法以重构模型类型和规划视点所用信息作为划分依据,将无模型的主动目标建模方法分为基于表面的主动目标建模方法、基于搜索的目标建模方法和两者相结合的方法 3大类,重点对前两类方法进行综述,首先解释了每类方法的基本思想,总结每类方法涉及的问题,然后对相关问题的主要研究方法进行归纳和分析,最后将各个问题的解决方法进行合理的搭配组合,形成不同的主动目标建模方法,并对各类方法的优势和局限性进行了总结。结果 各类主动目标建模算法在适用场景范围、计算复杂度等方面存在差异,但相对于传统的被动目标建模方法,当前的主动目标建模算法已经能够极大程度地提高建模任务的质量和降低建模所需代价。结论 基于表面的主动目标建模方法思想相对简单,但仅适用于表面简单的目标建模。基于搜索的目标建模方法能够量化地评价每一个候选视点,适用广泛且涉及的问题相对于基于表面的方法有更大的解决空间,有更多的研究成果产生。将二者涉及问题的不同研究方法相搭配,可以构成不同的主动目标建模方法子类

3D object recognition based on enhanced point pair features

针对基于原始点对特征的三维目标识别算法中存在的内存浪费、效率不高的问题,提出了一种基于增强型点对特征的三维目标识别算法。通过在原始点对特征的第四个分量上乘以一个符号函数,得到了一种区分性更强的点对的特征,消除了原始点对特征存在的二义性。考虑到待识别目标三维模型存在的自遮挡,利用点对之间的视点可见性约束,剔除了目标三维模型哈希表中存在的大量冗余点对,减小了内存开销并提高了三维目标识别算法的准确率和效率。在开放数据集以及实际采集的数据集上的实验结果表明,与基于原始点对特征的算法相比,本文提出的三维目标识别方法在识别准确率以及识别效率上都有一定程度的提升。</p

A Generic View Planning Algorithm Based on Formal Description of Perception Tasks

We propose a generic view planning algorithm that adjusts postures of the sensor automatically for multi-view perceptions. Formal description of the perception task is brought forward, including objects' prior information library, the perception status, and tasks' completion status. The view planning system operates on the basis of the formal expression, therefore it is not restricted by specific tasks. We employ the features that distinguish all candidate classes, together with features that define the rough shape of each class, as representation of the perceived information about the objects. The existence of features in each candidate class, the description of features, and the location relationship between features are known before perception, and they are filled in the fixed-form prior information library. All status are updated when data is received at a new view. To pick out the view that maximizes the acquisition of effective information, candidate views are sorted by a weighted evaluation function based on the updated status. Experiments of view planning for 3D recognition and reconstruction tasks are conducted, and the result shows that our algorithm has a good performance on multiple tasks

Continuous viewpoint planning in conjunction with dynamic exploration for active object recognition

Active object recognition (AOR) aims at collecting additional information to improve recognition performance by purposefully adjusting the viewpoint of an agent. How to determine the next best viewpoint of the agent, i.e., viewpoint planning (VP), is a research focus. Most existing VP methods perform viewpoint exploration in the discrete viewpoint space, which have to sample viewpoint space and may bring in significant quantization error. To address this challenge, a continuous VP approach for AOR based on reinforcement learning is proposed. Specifically, we use two separate neural networks to model the VP policy as a parameterized Gaussian distribution and resort the proximal policy optimization framework to learn the policy. Furthermore, an adaptive entropy regularization based dynamic exploration scheme is presented to automatically adjust the viewpoint exploration ability in the learning process. To the end, experimental results on the public dataset GERMS well demonstrate the superiority of our proposed VP method.</p

Active Object Recognition Based on Prior Feature Distribution Table

In this paper, an active object recognition (AOR) method based on a prior feature distribution table (PFDT) is proposed. According to the distribution information of predetermined features on each object in the model base, the prior feature distribution table is created. Taking it as input, a feature decision tree (FDT) is constructed for object recognition and viewpoint planning (VP). To determine the next best viewpoint, we transform it into an optimization problem that is solved with the tree dynamic programming algorithm. Experiments show that the proposed method can achieve the object recognition task with the minimum average number of viewpoints

Unified Optimization for Multiple Active Object Recognition Tasks with Feature Decision Tree

Visual object recognition plays an important role in the fields of computer vision and robotics. Static analysis of an image from a single viewpoint may not contain enough information to recognize an object unambiguously. Active object recognition (AOR) is aimed at collecting additional information to reduce ambiguity by purposefully adjusting the viewpoint of an observer. Existing AOR methods are oriented to a single task whose goal is to recognize an object by the minimum number of viewpoints. This paper presents a novel framework to deal with multiple AOR tasks based on feature decision tree (FDT). In the framework, in the light of the distribution of predetermined features on each object in a model base, a prior feature distribution table is firstly created as a kind of prior knowledge. Then it is utilized for the construction of FDT which describes the transition process of recognition states when different viewpoints are selected. Finally, in order to determine the next best viewpoints for the tasks with different goals, a unified optimization problem is established and solved by tree dynamic programming algorithm. In addition, the existing evaluation method of viewpoint planning (VP) efficiency is improved. According to whether the prior probability of the appearance of each object is known, the VP efficiency of different tasks is evaluated respectively. Experiments on the simulation and real environment show that the proposed framework obtains rather promising results in different AOR tasks.</p