Configurable Embodied Data Generation for Class-Agnostic RGB-D Video Segmentation

This letter presents a method for generating large-scale datasets to improve class-agnostic video segmentation across robots with different form factors. Specifically, we consider the question of whether video segmentation models trained on generic segmentation data could be more effective for parti...

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Vydané v:	IEEE robotics and automation letters Ročník 9; číslo 12; s. 11409 - 11416
Hlavní autori:	Opipari, Anthony, Krishnan, Aravindhan K, Gayaka, Shreekant, Sun, Min, Kuo, Cheng-Hao, Sen, Arnie, Jenkins, Odest Chadwicke
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.12.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Benchmark testing Cameras Data collection data sets for robotic vision Datasets Form factors Image segmentation Motion segmentation Object detection RGB-D perception Robot vision systems Robots segmentation and categorization Semantics Sensor placement Three-dimensional displays Trajectory Video data
ISSN:	2377-3766, 2377-3766
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