ArticleSensors (Basel, Switzerland)2021
Point Cloud Hand-Object Segmentation Using Multimodal Imaging with Thermal and Color Data for Safe Robotic Object Handover.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Robust Human-to-Robot Handover System Under Adverse Lighting.Biomimetics (Basel, Switzerland) · 2026Article
- TAMC: Textual Alignment and Masked Consistency for Open-Vocabulary 3D Scene Understanding.Sensors (Basel, Switzerland) · 2024Article
- Leveraging 3D convolutional neural network and 3D visible-near-infrared multimodal imaging for enhanced contactless oximetry.Journal of biomedical optics · 2024Article
- Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry.Sensors (Basel, Switzerland) · 2024Article
- Data Fusion of RGB and Depth Data with Image Enhancement.Journal of imaging · 2024Article
- OHO: A Multi-Modal, Multi-Purpose Dataset for Human-Robot Object Hand-Over.Sensors (Basel, Switzerland) · 2023Article
- Triangle-Mesh-Rasterization-Projection (TMRP): An Algorithm to Project a Point Cloud onto a Consistent, Dense and Accurate 2D Raster Image.Sensors (Basel, Switzerland) · 2023Article
- Food Image Segmentation Using Multi-Modal Imaging Sensors with Color and Thermal Data.Sensors (Basel, Switzerland) · 2023Article
- Interactive robot teaching based on finger trajectory using multimodal RGB-D-T-data.Frontiers in robotics and AI · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
This paper presents an application of neural networks operating on multimodal 3D data (3D point cloud, RGB, thermal) to effectively and precisely segment human hands and objects held in hand to realize a safe human-robot object handover. We discuss the problems encountered in building a multimodal sensor system, while the focus is on the calibration and alignment of a set of cameras including RGB, thermal, and NIR cameras. We propose the use of a copper-plastic chessboard calibration target with an internal active light source (near-infrared and visible light). By brief heating, the calibration target could be simultaneously and legibly captured by all cameras. Based on the multimodal dataset captured by our sensor system, PointNet, PointNet++, and RandLA-Net are utilized to verify the effectiveness of applying multimodal point cloud data for hand-object segmentation. These networks were trained on various data modes (XYZ, XYZ-T, XYZ-RGB, and XYZ-RGB-T). The experimental results show a significant improvement in the segmentation performance of XYZ-RGB-T (mean Intersection over Union: 82.8% by RandLA-Net) compared with the other three modes (77.3% by XYZ-RGB, 35.7% by XYZ-T, 35.7% by XYZ), in which it is worth mentioning that the Intersection over Union for the single class of hand achieves 92.6%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.