首页|New Findings from Rochester Institute for Technology in the Area of Robotics Reported (Multi-scale Progressive Fusion-based Depth Image Completion and Enhancement for Industrial Collaborative Robot Applications)
New Findings from Rochester Institute for Technology in the Area of Robotics Reported (Multi-scale Progressive Fusion-based Depth Image Completion and Enhancement for Industrial Collaborative Robot Applications)
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NETL
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Springer Nature
Fresh data on Robotics are presented in a new report. According to news reporting originating in Rochester, New York, by NewsRx journalists, research stated, "The depth image obtained by consumer-level depth cameras generally has low resolution and missing regions due to the limitations of the depth camera hardware and the method of depth image generation. Despite the fact that many studies have been done on RGB image completion and super-resolution, a key issue with depth images is that there will be evident jagged boundaries and a significant loss of geometric information." Financial support for this research came from German Research Foundation (DFG). The news reporters obtained a quote from the research from Rochester Institute for Technology, "To address these issues, we introduce a multi-scale progressive fusion network for depth image completion and super-resolution in this paper, which has an asymptotic structure for integrating hierarchical features in different domains. We employ two separate branches to learn the features of a multi-scale image given a depth image and its corresponding RGB image. The extracted features are then fused into different level features of these two branches using a step-by-step strategy to recreate the final depth image. To confine distinct borders and geometric features, a multi-dimension loss is also designed. Extensive depth completion and super-resolution studies reveal that our proposed method outperforms state-of-the-art methods both qualitatively and quantitatively. The proposed methods are also applied to two human-robot interaction applications, including a remote-controlled robot based on an unmanned ground vehicle (UGV), AR-based toolpath planning, and automatic toolpath extraction."
RochesterNew YorkUnited StatesNorth and Central AmericaEmerging TechnologiesMachine LearningRobotRoboticsRochester Institute for Technology
NETL
NSTL
Springer Nature
