Yongzhi Su, Yan Di, Guangyao Zhai, Fabian Manhardt, Jason Rambach, Benjamin Busam, Didier Stricker and Federico Tombari “ OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection.” IEEE Robotics and Automation Letters (2023). Extensive experiments on the public benchmark demonstrate that OPA-3D outperforms state-of-the-art methods on the main Car category, whilst keeping a real-time inference speed. This novel two-stream representation enables us to enforce cross-stream consistency terms, which aligns the outputs of both streams, and further improves the overall performance. The second stream, named as the Context Stream, directly regresses 3D object location and size. In addition, a bounding box based geometry projection scheme is employed in an effort to enhance distance perception. Thereby, the geometry stream combines visible depth and depth-bounding box residuals to recover the object bounding box via explicit occlusion-aware optimization. To overcome this limitation, we instead propose to jointly estimate dense scene depth with depth-bounding box residuals and object bounding boxes, allowing a two-stream detection of 3D objects that harnesses both geometry and context information. Yet, such two-stage methods typically suffer from overfitting and are incapable of explicitly encapsulating the geometric relation between depth and object bounding box. The article is openly accessible at: Ībstract: Monocular 3D object detection has recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery. The work is a collaboration of DFKI with the TU Munich and Google. We are happy to announce that our article “ OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection” was published in the prestigious IEEE Robotics and Automation Letters (RA-L) Journal. Dimitrios Giakoumis (CERTH ITI) – Designing human-robot interaction interfaces for shotcrete construction robots the RobetArme project case.Patricia Helen Rosen (BAUA) – Design recommendations for construction robots – a human-centred perspective. ![]() Jason Rambach (DFKI) – Machine perception for human-robot handover scenarios in construction.Serena Ivaldi (INRIA) – Teleoperating a robot for removing asbestos tiles on roofs: Insights from a pilot study. ![]() The program of the special session included the following talks: The organization of the special session was done by Jason Rambach, Gabor Sziebig, Research Manager at SINTEF, and Mihoko Niitsuma, Professor at Chuo University. Jason Rambach, coordinator of the EU Horizon Project HumanTech co-organized a special session on “Human Factors in Construction Robotics” at the IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO 2023) in Berlin, Germany (5.6-7.6).
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