Results 1 - 10 of 108

Real-time human pose recognition in parts from single depth images

by Jamie Shotton, Andrew Fitzgibbon, Mat Cook, Toby Sharp, Mark Finocchio, Richard Moore, Alex Kipman, Andrew Blake - IN CVPR , 2011
"... We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler p ..."
Abstract - Cited by 568 (17 self) - Add to MetaCart
We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler

Learning depth from single monocular images

by Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng - In NIPS 18 , 2005
"... We consider the task of depth estimation from a single monocular image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured outdoor environments which include forests, trees, buildings, etc.) and their correspond ..."
Abstract - Cited by 132 (34 self) - Add to MetaCart
We consider the task of depth estimation from a single monocular image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured outdoor environments which include forests, trees, buildings, etc

Recovering 3D Human Pose from Monocular Images

by Ankur Agarwal, Bill Triggs
"... We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descrip ..."
Abstract - Cited by 261 (0 self) - Add to MetaCart
from single silhouettes ambiguous. We propose two solutions to this: the first embeds the method in a tracking framework, using dynamics from the previous state estimate to disambiguate the pose; the second uses a mixture of regressors framework to return multiple solutions for each silhouette. We show

3-D depth reconstruction from a single still image

by A. Saxena, S. H. Chung, A. Y. Ng , 2006
"... We consider the task of 3-d depth estimation from a single still image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured indoor and outdoor environments which include forests, sidewalks, trees, buildings, etc ..."
Abstract - Cited by 114 (17 self) - Add to MetaCart
We consider the task of 3-d depth estimation from a single still image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured indoor and outdoor environments which include forests, sidewalks, trees, buildings

Make3D: Learning 3D Scene Structure from a Single Still Image

by Ashutosh Saxena, Min Sun, Andrew Y. Ng
"... We consider the problem of estimating detailed 3-d structure from a single still image of an unstructured environment. Our goal is to create 3-d models which are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov Random Field (M ..."
Abstract - Cited by 158 (19 self) - Add to MetaCart
We consider the problem of estimating detailed 3-d structure from a single still image of an unstructured environment. Our goal is to create 3-d models which are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov Random Field

Depth map prediction from a single image using a multi-scale deep network

by David Eigen, Christian Puhrsch, Rob Fergus - NIPS
"... Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring in-tegration of both global and local information from various ..."
Abstract - Cited by 26 (2 self) - Add to MetaCart
Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring in-tegration of both global and local information from various

Deep convolutional neural fields for depth estimation from a single image

by Fayao Liu, Chunhua Shen, Guosheng Lin - In CVPR , 2015
"... We consider the problem of depth estimation from a sin-gle molecular image in this work. It is a challenging task as no reliable depth cues are available, e.g., stereo correspon-dences, motions etc. Previous efforts have been focusing on exploiting geometric priors or additional sources of in-format ..."
Abstract - Cited by 4 (1 self) - Add to MetaCart
be naturally formulated into a continuous con-ditional random field (CRF) learning problem. Therefore, we in this paper present a deep convolutional neural field model for estimating depths from a single image, aiming to

Efficient Human Pose Estimation from Single Depth Images

by Jamie Shotton, Ross Girshick, Andrew Fitzgibbon, Toby Sharp, Mat Cook, Mark Finocchio, Richard Moore, Pushmeet Kohli, Antonio Criminisi, Alex Kipman, Andrew Blake - INVITED PAPER- CVPR 2011 SPECIAL ISSUE , 2011
"... We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image, without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set of training ..."
Abstract - Cited by 37 (4 self) - Add to MetaCart
We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image, without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set

Non-parametric Depth Estimation for Images from a Single Reference Depth

by Zhi Yang, Varun Chandola , 2014
"... We present a non-parametric method for estimating depth of a single still image. We start from a single reference image and its corresponding 3-d depth and use an unsupervised neural network to transform the reference depth to represent the target image. In doing so, we attempt to mimic the human vi ..."
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We present a non-parametric method for estimating depth of a single still image. We start from a single reference image and its corresponding 3-d depth and use an unsupervised neural network to transform the reference depth to represent the target image. In doing so, we attempt to mimic the human

Make3D: Depth Perception from a Single Still Image

by Ashutosh Saxena, Min Sun, Andrew Y. Ng
"... Humans have an amazing ability to perceive depth from a single still image; however, it remains a challenging problem for current computer vision systems. In this paper, we will present algorithms for estimating depth from a single still image. There are numerous monocular cues—such as texture varia ..."
Abstract - Cited by 9 (1 self) - Add to MetaCart
variations and gradients, defocus, color/haze, etc.—that can be used for depth perception. Taking a supervised learning approach to this problem, in which we begin by collecting a training set of single images and their corresponding groundtruth depths, we learn the mapping from image features to the depths
Results 1 - 10 of 108