FastV2C-HandNet: Fast Voxel to Coordinate Hand Pose Estimation with 3D Convolutional Neural Networks

@article{Lekhwani2019FastV2CHandNetFV,
  title={FastV2C-HandNet: Fast Voxel to Coordinate Hand Pose Estimation with 3D Convolutional Neural Networks},
  author={Rohan Lekhwani},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.06327}
}
Hand pose estimation from monocular depth images has been an important and challenging problem in the Computer Vision community. In this paper, we present a novel approach to estimate 3D hand joint locations from 2D depth images. Unlike most of the previous methods, our model using a voxel to coordinate(V2C) approach captures the 3D spatial information from a depth image using 3D CNNs thereby giving it a greater understanding of the input. We voxelize the input depth map to capture the 3D… 

Human Pose Estimation Using Per-Point Body Region Assignment

This paper introduces a novel two-stage deep learning approach named Segmentation-Guided Pose Estimation (SGPE), based on two neural networks working in a sequential fashion, while both models effectively process unorganized point clouds on the input.

References

SHOWING 1-10 OF 55 REFERENCES

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

This model is designed as a 3D CNN that provides accurate estimates while running in real-time and outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based3D hand pose estimation challenge.

3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation from Single Depth Images

Experiments show that the proposed 3D CNN based approach outperforms state-of-the-art methods on two challenging hand pose datasets, and is very efficient as the implementation runs at over 215 fps on a standard computer with a single GPU.

Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals

This paper investigates the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction.

Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs

This work proposes to first project the query depth image onto three orthogonal planes and utilize these multi-view projections to regress for 2D heat-maps which estimate the joint positions on each plane to produce final 3D hand pose estimation with learned pose priors.

End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data

Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets.

Point-to-Point Regression PointNet for 3D Hand Pose Estimation

The proposed Point-to-Point Regression PointNet directly takes the 3D point cloud as input and outputs point-wise estimations, i.e., heat-maps and unit vector fields on the point cloud, representing the closeness and direction from every point in the pointCloud to the hand joint.

Generalized Feedback Loop for Joint Hand-Object Pose Estimation

This work shows that it can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop, and outperforms state-of-the-art methods for joint hand-object pose estimation when using depth images only.

Hand Pose Estimation via Latent 2.5D Heatmap Regression

This paper proposes a new method for 3D hand pose estimation from a monocular image through a novel 2.5D pose representation that implicitly learns depth maps and heatmap distributions with a novel CNN architecture.

Region ensemble network: Improving convolutional network for hand pose estimation

A tree-structured Region Ensemble Network (REN), which partitions the convolution outputs into regions and integrates the results from multiple regressors on each regions and achieves the best performance among state-of-the-arts on two public datasets.

HBE: Hand Branch Ensemble Network for Real-Time 3D Hand Pose Estimation

The experimental results demonstrate that the novel three-branch Convolutional Neural Networks named Hand Branch Ensemble network achieves comparable or better performance to state-of-the-art methods with less training data, shorter training time and faster frame rate.
...