文件名称:
3D Pose Estimation and 3D Model Retrieval for Objects in the Wild
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文件大小: 1mb
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上传时间: 2018-07-04
详细说明： We propose a scalable, efficient and accurate approach to retrieve 3D models for objects in the wild. Our contri- bution is twofold. We first present a 3D pose estimation approach for object categories which significantly outper- forms the state-of-the-art on Pascal3D+. Second, we use the estimated pose as a prior to retrieve 3D models which accurately represent the geometry of objects in RGB im- ages. For this purpose, we render depth images from 3D models under our predicted pose and match learned im- age descriptors of RGB im ages against those of rendered depth images using a CNN-based multi-view metric learn- ing approach. In this way, we are the first to report quanti- tative results for 3D model retrieval on Pascal3D+, where our method chooses the same models as human annota- tors for 50% of the validation images on average. In ad- dition, we show that our method, which was trained purely on Pascal3D+, retrieves rich and accurate 3D models from ShapeNet given RGB images of objects in the wild. ...展开收缩
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