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首页> 外文期刊>International Journal of Computer Vision >3DNN: 3D Nearest Neighbor Data-Driven Geometric Scene Understanding Using 3D Models
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3DNN: 3D Nearest Neighbor Data-Driven Geometric Scene Understanding Using 3D Models

机译：3DNN：使用3D模型理解3D最近邻数据驱动的几何场景

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摘要

In this paper, we describe a data-driven approach to leverage repositories of 3D models for scene understanding. Our ability to relate what we see in an image to a large collection of 3D models allows us to transfer information from these models, creating a rich understanding of the scene. We develop a framework for auto-calibrating a camera, rendering 3D models from the viewpoint an image was taken, and computing a similarity measure between each 3D model and an input image. We demonstrate this data-driven approach in the context of geometry estimation and show the ability to find the identities, poses and styles of objects in a scene. The true benefit of 3DNN compared to a traditional 2D nearest-neighbor approach is that by generalizing across viewpoints, we free ourselves from the need to have training examples captured from all possible viewpoints. Thus, we are able to achieve comparable results using orders of magnitude less data, and recognize objects from never-before-seen viewpoints. In this work, we describe the 3DNN algorithm and rigorously evaluate its performance for the tasks of geometry estimation and object detection/segmentation, as well as two novel applications: affordance estimation and photorealistic object insertion.
机译：在本文中，我们描述了一种数据驱动的方法，以利用3D模型的存储库进行场景理解。我们将图像中看到的内容与大量3D模型相关联的能力使我们能够从这些模型中传输信息，从而对场景产生丰富的了解。我们开发了一个框架，用于自动校准相机，从拍摄图像的角度渲染3D模型，以及计算每个3D模型与输入图像之间的相似度。我们在几何估计的背景下演示了这种数据驱动的方法，并展示了找到场景中对象的标识，姿势和样式的能力。与传统的2D最近邻方法相比，3DNN的真正好处在于，通过对各种观点进行概括，我们无需从所有可能的观点中获取训练示例。因此，我们能够使用较少数量级的数据获得可比的结果，并从前所未有的角度识别物体。在这项工作中，我们描述了3DNN算法，并严格评估了其在几何估计和对象检测/细分以及两个新颖的应用程序中的性能：可负担性估计和真实感对象插入。

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