XU Chi

Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups

Published date : 12 Aug 2016

Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately. In this paper, a unied paradigm based on Lie group theory is proposed, which enables us to collectively address these related problems. Our approach is also applicable to a wide range of articulated objects. Empirically it is evaluated on lab animals including mouse and sh, as well as on human hand. On
these applications, it is shown to deliver competitive results compared to the state-of-the-arts, and non-trivial

type
Journal Paper
journal
International Journal of Computer Vision, 2016
Impact Factor
4.207

Pose Estimation from Line Correspondences: A Complete Analysis and A Series of Solutions

Published date : 20 Jun 2016

In this paper we deal with the camera pose estimation problem from a set of 2D/3D line correspondences, which is also known as PnL (Perspective-n-Line) problem. We carry out our study by comparing PnL with the well-studied PnP (Perspective-n-Point) problem, and our contributions are threefold: (1) We provide a complete 3D configuration analysis for P3L, which includes the well-known P3P problem as well as several existing analyses as special cases.

type
Journal Paper
journal
IEEE Transactions on Pattern Analysis and Machine Intelligence, Issue 99, 2016, doi: 10.1109/TPAMI.2016.2582162
Impact Factor
6.077

GHand: A GPU algorithm for realtime hand pose estimation using depth camera

Published date : 04 May 2015

We present GHand, a GPU algorithm for markerless hand pose estimation from a single depth image obtained from a commodity depth camera. Our method uses a dual random forest approach: the first forest estimates position and orientation of hand in 3D, while the second forest determines the joint angles of the kinematic chain of our hand model. GHand runs entirely on GPU, at a speed of 64 FPS with an average 3D joint position error of 20mm. It can detect complex poses with interlocked and occluded fingers and hidden fingertips.

type
Conference Paper/Poster
journal
EuroGraphics 2015, 4th - 8th May 2015, Kongresshaus Zurich, Switzerland

Estimate Hand Poses Efficiently from Single Depth Images

Published date : 19 Apr 2015

This paper aims to tackle the practically very challenging problem of efficient and accurate hand pose estimation from single depth images. A dedicated two-step regression forest pipeline is proposed: Given an input hand depth image, step one involves mainly estimation of 3D location and in-plane rotation of the hand using a pixel-wise regression forest. This is utilized in step two which delivers final hand estimation by a similar regression forest model based on the entire hand image patch. Moreover, our estimation is guided by internally executing a 3D hand kinematic chain model.

type
Journal Paper
journal
International Journal of Computer Vision (IJCV), Vol. 112, No. 3, May 2015, pp.1-25
Impact Factor
3.81

Efficient Hand Pose Estimation from a Single Depth Image

Published date : 01 Dec 2013

We tackle the practical problem of hand pose estimation from a single noisy depth image. A dedicated three-step pipeline is proposed: Initial estimation step provides an initial estimation of the hand in-plane orientation and 3D location; Candidate generation step produces a set of 3D

type
Conference Paper/Poster
journal
International Conference on Computer Vision (ICCV), 1-8 Dec 2013, Sydney, Australia

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