V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D depth map. While the depth map is intrinsically 3D data, many previous methods treat depth maps as 2D images that can distort the shape of the actual object through projection from 3D to 2D space. This compels the network to perform perspective distortion-invariant estimation. The second weakness of the conventional approach is that directly regressing 3D coordinates from a 2D image is a highly non-linear mapping, which causes difficulty in the learning procedure. To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint. We design our model as a 3D CNN that provides accurate estimates while running in real-time. Our system outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge. The code is available in https://github.com/mks0601/V2V-PoseNet_RELEASE.Comment: HANDS 2017 Challenge Frame-based 3D Hand Pose Estimation Winner (ICCV 2017), Published at CVPR 201

Nonlinear autopilot design for endo- and exo-atmospheric interceptor with thrust-vector-control

This paper proposes an autopilot design for an interceptor with Thrust-Vector-Control (TVC) that operates in the endo- and exo-atmospheric regions. The main objective of the proposed autopilot design is to ensure control performance in both atmospheric regions, without changing the control mechanism. In this paper, the characteristics of the aerodynamic forces in both atmospheric regions are first investigated to examine the issue of the conventional control mechanism at various altitudes. And then, a control mechanism, which can be applied to both atmospheric regions, is determined based on the analysis results. An autopilot design is then followed by utilizing the control mechanism and the feedback linearization control (FBLC) method. Accordingly, the proposed autopilot does not rely on changing the control mechanism depending on flight condition unlike the conventional approach as well as it can adjust the control gains automatically according to the changes of flight operating conditions. In this paper, the robustness of the proposed autopilot is investigated through the tracking error analysis and the relative stability analysis in the presence of model uncertainties. The physical meaning of the proposed autopilot is also presented by comparing to the well-known three-loop control structure. Finally, numerical simulations are performed to show the performance of the proposed method