Material-Based Segmentation of Objects

Stets, Jonathan Dyssel; Lyngby, Rasmus Ahrenkiel; Frisvad, Jeppe Revall; Dahl, Anders Bjorholm

doi:10.1007/978-3-030-20205-7_13

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Synthetic data, and CGI images, are extensively used in training machine learning for computer vision [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. CGI data is created by combining human-made assets [35][36][37][38][39][40], simulation, and procedural generation rules.…”

Section: Synthetic Data and Domain Gapmentioning

confidence: 99%

“…This approach is limited by the cost of human labor and by the fact that humans perform poorly in segmenting scatter patterns (cracks, drops) as well as soft boundaries and gradual transitions between states [5]. The alternative approach uses synthetic data using CGI and simulation [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. This approach has no limitation on the amount of data it can generate and the precision of the annotations but is limited to procedural generation roles that cannot capture the vast complexity and patterns of the real world [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

Eppel

Xu²,

Bismuth³

et al. 2020

Preprint

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This work presents a machine learning approach for computer vision-based recognition of materials inside vessels in a chemistry lab and other settings. In addition, we release a dataset associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their content is essential for performing this task. Modern machine vision methods learn recognition tasks by using datasets containing a large number of annotated images. This work presents the Vector-LabPics dataset, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder,...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this dataset. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.

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Section: Synthetic Data and Domain Gapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

Eppel

Xu²,

Bismuth³

et al. 2020

Preprint

View full text Add to dashboard Cite

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Clear Grasp: 3D Shape Estimation of Transparent Objects for Manipulation

Sajjan

Moore

Pan

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

182

207

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ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation

Sajjan¹,

Moore²,

Pan³

et al. 2019

Preprint

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Transparent objects are a common part of everyday life, yet they possess unique visual properties that make them incredibly difficult for standard 3D sensors to produce accurate depth estimates for. In many cases, they often appear as noisy or distorted approximations of the surfaces that lie behind them. To address these challenges, we present ClearGrasp -a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation. Given a single RGB-D image of transparent objects, ClearGrasp uses deep convolutional networks to infer surface normals, masks of transparent surfaces, and occlusion boundaries. It then uses these outputs to refine the initial depth estimates for all transparent surfaces in the scene. To train and test ClearGrasp, we construct a large-scale synthetic dataset of over 50,000 RGB-D images, as well as a real-world test benchmark with 286 RGB-D images of transparent objects and their ground truth geometries. The experiments demonstrate that ClearGrasp is substantially better than monocular depth estimation baselines and is capable of generalizing to realworld images and novel objects. We also demonstrate that ClearGrasp can be applied out-of-the-box to improve grasping algorithms' performance on transparent objects. Code, data, and benchmarks will be released. Supplementary materials: https://sites.google.com/view/cleargrasp 1

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Material-Based Segmentation of Objects

Cited by 3 publications

References 24 publications

Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

Clear Grasp: 3D Shape Estimation of Transparent Objects for Manipulation

ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation

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