Discriminatively-guided Deliberative Perception for Pose Estimation of Multiple 3D Object Instances
Discriminatively-guided Deliberative Perception for Pose Estimation of Multiple 3D Object Instances
复制标题
用于多个 3D 对象实例姿态估计的判别引导深思熟虑感知
DOI：
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发表时间：
2016
期刊：
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通讯作者：
Maxim Likhachev
中科院分区：
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作者：
V. Narayanan;Maxim Likhachev
我们从3D传感器数据中介绍了一种基于模型的多对象识别和3个DOF姿势估算的新颖范式，该数据将详尽的全球推理与歧视训练的算法集成在一起，以本任务的典型方法。在大型注释场景的数据库中训练的统计学习者的功能匹配或回归。培训数据，另一方面，基于渲染和验证的方法是可靠的，不需要训练，但是我们可以通过测试时间来猜测。通用方法和歧视训练的方法。 D2P是一种单一的搜索算法，它寻找与输入相匹配的场景的“最佳”渲染，b）可以通过任何和多个判别算法来指导，并且c）生成了相对于选择适当界限的解决方案成本功能。此外，我们介绍了多对象估计问题的完整性和解决方案的说明，并证明D2P已完成。基准数据集研究D2P与现有方法有关的各个方面。
We introduce a novel paradigm for model-based multi-object recognition and 3 DoF pose estimation from 3D sensor data that integrates exhaustive global reasoning with discriminatively-trained algorithms in a principled fashion. Typical approaches for this task are based on scene-to-model feature matching or regression by statistical learners trained on a large database of annotated scenes. These approaches are fast but sensitive to occlusions, features, and/or training data. Generative approaches, on the other hand, e.g., methods based on rendering and verification, are robust to occlusions and require no training, but are slow at test time. We conjecture that robust and efficient perception can be achieved through a combination of generative methods and discriminatively-trained approaches. To this end, we introduce the Discriminatively-guided Deliberative Perception (D2P) paradigm that has the following desirable properties: a) D2P is a single search algorithm that looks for the ‘best’ rendering of the scene that matches the input, b) can be guided by any and multiple discriminative algorithms, and c) generates a solution that is provably bounded suboptimal with respect to the chosen cost function. In addition, we introduce the notions of completeness and resolution completeness for multi-object pose estimation problems, and show that D2P is resolution complete. We conduct extensive evaluations on a benchmark dataset to study various aspects of D2P in relation to existing approaches.