Pith. sign in

REVIEW 5 cited by

3D Hand Pose Tracking and Estimation Using Stereo Matching

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1610.07214 v1 pith:TMD5BYXU submitted 2016-10-23 cs.CV

Jiawei Zhang , Jianbo Jiao , Mingliang Chen , Liangqiong Qu , Xiaobin Xu , Qingxiong Yang This is my paper · ORCID
classification cs.CV
keywords handtrackingestimationalgorithmsproposedstereoactivedepth
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

3D hand pose tracking/estimation will be very important in the next generation of human-computer interaction. Most of the currently available algorithms rely on low-cost active depth sensors. However, these sensors can be easily interfered by other active sources and require relatively high power consumption. As a result, they are currently not suitable for outdoor environments and mobile devices. This paper aims at tracking/estimating hand poses using passive stereo which avoids these limitations. A benchmark with 18,000 stereo image pairs and 18,000 depth images captured from different scenarios and the ground-truth 3D positions of palm and finger joints (obtained from the manual label) is thus proposed. This paper demonstrates that the performance of the state-of-the art tracking/estimation algorithms can be maintained with most stereo matching algorithms on the proposed benchmark, as long as the hand segmentation is correct. As a result, a novel stereo-based hand segmentation algorithm specially designed for hand tracking/estimation is proposed. The quantitative evaluation demonstrates that the proposed algorithm is suitable for the state-of-the-art hand pose tracking/estimation algorithms and the tracking quality is comparable to the use of active depth sensors under different challenging scenarios.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos

    cs.CV 2024-11 conditional novelty 7.0 of 10

    HOT3D releases 833 minutes of hardware-synchronized, egocentric multi-view video from real headsets with motion-capture ground truth for hands and objects, and shows multi-view baselines outperform single-view baselin...

  2. HandOcc: NeRF-based Hand Rendering with Occupancy Networks

    cs.CV 2025-05 conditional novelty 6.0 of 10

    HandOcc renders articulated hands with a NeRF conditioned on skeletal occupancy instead of a MANO mesh, achieving competitive results on InterHand2.6M.

  3. VEPHand: View-Efficient Photometric Hand Performance Capture at Scale

    cs.CV 2026-06 unverdicted novelty 5.0 of 10

    End-to-end neural pipeline extracts hand geometry from unmasked limited-view images and registers it to a personalized tetrahedral model via volumetric offsets, achieving SOTA on over 12,000 sequences.

  4. Self-supervised Latent Space Optimization with Nebula Variational Coding

    cs.LG 2025-06 conditional novelty 4.0 of 10

    Adding trainable nebula anchors plus pseudo-label metric learning to a VAE-style latent space improves downstream performance on text, image, 3D point cloud and volumetric benchmarks.

  5. Survey on Hand Gesture Recognition from Visual Input

    cs.CV 2025-01 conditional novelty 4.0 of 10

    A structured survey of recent hand gesture recognition from camera input, with a taxonomy of methods, datasets, metrics, and open problems.

Pith tools