说明： We present a fast inverse-graphics framework for instance-level 3D scene understanding. We train a deep convolutional network that learns to map image regions to the full 3D shape and pose of all object instances in the image. Our method produces a <algofei> 在 上传 | 大小：3145728
说明： Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos), many other data sources are inherently sparse. Examples include 3D <algofei> 在 上传 | 大小：630784
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- <algofei> 在 上传 | 大小：1048576
说明： We develop a 3D object detection algorithm that uses latent support surfaces to capture contextual relationships in indoor scenes. Existing 3D representations for RGB-D images capture the local shape and appearance of object categories, but have lim <algofei> 在 上传 | 大小：2097152
说明： We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism. The tasks present several challenges: a large dataset with long videos, a larg <algofei> 在 上传 | 大小：1048576
说明： Recently, remarkable advances have been achieved in 3D human pose estimation from monocular images because of the powerful Deep Convolutional Neural Networks (DC- NNs). Despite their success on large-scale datasets col- lected in the constrained lab <algofei> 在 上传 | 大小：1048576
说明： Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we pro- pose a multitask framework for jointly 2D and 3D pose estimation from still images an <algofei> 在 上传 | 大小：1048576
