2022
DOI: 10.1007/s11263-022-01740-3
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RM3D: Robust Data-Efficient 3D Scene Parsing via Traditional and Learnt 3D Descriptors-Based Semantic Region Merging

Abstract: Existing state-of-the-art 3D point cloud understanding methods merely perform well in a fully supervised manner. To the best of our knowledge, there exists no unified framework that simultaneously solves the downstream high-level understanding tasks including both segmentation and detection, especially when labels are extremely limited. This work presents a general and simple framework to tackle point cloud understanding when labels are limited. The first contribution is that we have done extensive methodology… Show more

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Cited by 8 publications
(4 citation statements)
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“…Finally, we have integrated our proposed approach with other robotics modules such as SLAM and motion/task planning as a whole system to perform UAV tracking and landing in real applications. UAV Vision is important [34], [35]. Our integrated visual system is very important for future UAV-based detection and tracking applications such as autonomous landing on moving vehicles.…”
Section: Discussionmentioning
confidence: 99%
“…Finally, we have integrated our proposed approach with other robotics modules such as SLAM and motion/task planning as a whole system to perform UAV tracking and landing in real applications. UAV Vision is important [34], [35]. Our integrated visual system is very important for future UAV-based detection and tracking applications such as autonomous landing on moving vehicles.…”
Section: Discussionmentioning
confidence: 99%
“…Various visual sensors and Light Detection And Ranging Sensors (LiDAR) sensors have been widely deployed in various robotics platforms such as automatically navigated aerial robots and ground robots [16], [17], [18], [19], [20], [21]. For example, the SLAM is widely adopted for robot localization, mapping, and navigation applications [11], [22], [23], [12], [24], [25], [26], [27], [28], [29], [30], [31], [22].…”
Section: B the Overall System For Uav Inspectionsmentioning
confidence: 99%
“…On the other hand, LRA provides information exclusively along radial and elevation directions. Despite LRF's capability to furnish information for the entire local space, its repeatability along the x and y axes notably lags behind that along the Z-axis [12] , resulting in an overall repeatability markedly inferior to that of LRA. Spatial information encoded through the LRA-based approach attains heightened precision when compared to the LRF-centric methodology.…”
Section: Introductionmentioning
confidence: 97%