RT-less: a multi-scene RGB dataset for 6D pose estimation of reflective texture-less objects

Zhao, Xinyue; Li, Quanzhi; Chao, Yue; Wang, Quanyou; He, Zaixing; Liang, Dong

doi:10.1007/s00371-023-03097-1

Vis Comput

2023

DOI: 10.1007/s00371-023-03097-1

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RT-less: a multi-scene RGB dataset for 6D pose estimation of reflective texture-less objects

Xinyue Zhao,

Quanzhi Li,

Yue Chao

et al.

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2024

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CurveML: a benchmark for evaluating and training learning-based methods of classification, recognition, and fitting of plane curves

Raffo,

Ranieri,

Romanengo

et al. 2024

Vis Comput

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We propose CurveML, a benchmark for evaluating and comparing methods for the classification and identification of plane curves represented as point sets. The dataset is composed of 520k curves, of which 280k are generated from specific families characterised by distinctive shapes, and 240k are obtained from Bézier or composite Bézier curves. The dataset was generated starting from the parametric equations of the selected curves making it easily extensible. It is split into training, validation, and test sets to make it usable by learning-based methods, and it contains curves perturbed with different kinds of point set artefacts. To evaluate the detection of curves in point sets, our benchmark includes various metrics with particular care on what concerns the classification and approximation accuracy. Finally, we provide a comprehensive set of accompanying demonstrations, showcasing curve classification, and parameter regression tasks using both ResNet-based and PointNet-based networks. These demonstrations encompass 14 experiments, with each network type comprising 7 runs: 1 for classification and 6 for regression of the 6 defining parameters of plane curves. The corresponding Jupyter notebooks with training procedures, evaluations, and pre-trained models are also included for a thorough understanding of the methodologies employed.

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CurveML: a benchmark for evaluating and training learning-based methods of classification, recognition, and fitting of plane curves

Raffo,

Ranieri,

Romanengo

et al. 2024

Vis Comput

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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RT-less: a multi-scene RGB dataset for 6D pose estimation of reflective texture-less objects

Cited by 1 publication

References 32 publications

CurveML: a benchmark for evaluating and training learning-based methods of classification, recognition, and fitting of plane curves

CurveML: a benchmark for evaluating and training learning-based methods of classification, recognition, and fitting of plane curves

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