Robust 3D Hand Pose Estimation in Single Depth Images: from Single-View CNN to Multi-View CNNs (1606.07253v3)

Abstract: Articulated hand pose estimation plays an important role in human-computer interaction. Despite the recent progress, the accuracy of existing methods is still not satisfactory, partially due to the difficulty of embedded high-dimensional and non-linear regression problem. Different from the existing discriminative methods that regress for the hand pose with a single depth image, we propose 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. These multi-view heat-maps are then fused to produce final 3D hand pose estimation with learned pose priors. Experiments show that the proposed method largely outperforms state-of-the-art on a challenging dataset. Moreover, a cross-dataset experiment also demonstrates the good generalization ability of the proposed method.

• The paper introduces a multi-view CNN framework that projects depth images onto three orthogonal planes to create heat-maps, improving 3D hand pose estimation.
• The method fuses multi-view heat-maps and integrates PCA-based constraints to resolve ambiguities and refine joint location predictions.
• Empirical results demonstrate that the approach outperforms single-view methods, achieving robust accuracy and real-time performance above 70 fps.

An Expert Analysis of "Robust 3D Hand Pose Estimation in Single Depth Images: from Single-View CNN to Multi-View CNNs"

This paper presents a novel method for 3D hand pose estimation from single depth images using convolutional neural networks (CNNs). The key advancement proposed is the multi-view CNN approach, which significantly enhances estimation accuracy compared to traditional single-view methods. The authors introduce a framework that projects depth images onto three orthogonal planes, creating multi-view heat-maps that are subsequently fused to achieve precise 3D joint estimations.

Summary of Contributions

1. Multi-View Heat-Map Generation: The paper proposes projecting 3D points from depth images onto three orthogonal planes - x-y, y-z, z-x - to generate projected images. Each projection is processed with a separate CNN to produce 2D heat-maps that reflect joint position probabilities on each plane. The multi-view strategy addresses depth estimation challenges typically encountered in single-view methods, which often rely on a single 2D projection plane.
2. Fusion of Multi-View Heat-Maps: By leveraging information from all three projections, the authors employ a fusion method that combines the heat-maps to gain full 3D joint location information. This process integrates learned pose priors to refine estimates and resolve ambiguities inherent in single-view models.
3. PCA-Based Hand Pose Constraints: The work incorporates principal component analysis (PCA) to impose constraints on hand motion, minimizing estimation ambiguities by projecting the joint locations onto a learned low-dimensional subspace. The use of PCA allows implicit hand pose constraint enforcement without requiring explicit size and motion definitions.
4. Empirical Performance Evaluation: Through comprehensive testing on established datasets, the paper demonstrates that the proposed multi-view CNN approach outperforms state-of-the-art methods, with a runtime speed exceeding 70fps. The cross-dataset evaluation confirms the method's generalization capacity and robust performance across diverse scenarios and datasets.

Numerical Results

The proposed method achieves notable mean error distances, with the multi-view fine fusion method reducing errors significantly compared to single-view approaches. The worst-case accuracy measures indicate substantial improvements, with a reported runtime capable of real-time application.

Theoretical and Practical Implications

This research contributes to the theoretical understanding of CNN-based regression problems in 3D space by showcasing how multi-view heat-maps enhance depth estimation accuracy. Practically, this method facilitates accurate, real-time 3D hand tracking, which is essential in human-computer interaction applications like virtual and augmented reality. Future developments might explore further refinement of the fusion algorithms, integrate temporal information for tracking robustness, or extend to other articulated objects.

In conclusion, this work effectively leverages the strengths of the multi-view perspective and CNNs to advance the field of hand pose estimation, offering both improved accuracy and practical utility in dynamic environments.