Multimodal Data
This dataset comprises images of parts from real industrial scenarios and virtual reality environments. Real images are sourced from actual industrial settings, ensuring both authenticity and diversity, while virtual reality images, which make up approximately 11% of the dataset, are captured through precise 3D modeling. Approximately 30% of the part information was manually authored by industry experts, while the remaining 70% was generated by multimodal large models such as Wenxin Yiyan and GPT-4.
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The increasing availability of multimodal data holds many promises for developments in millimeter-wave (mmWave) multiple-antenna systems by harnessing the potential for enhanced situational awareness. Specifically, inclusion of non-RF modalities to complement RF-only data in communications-related decisions like beam selection may speed up decision making in situations where an exhaustive search, spanning all candidate options, is required by the standard. However, to accelerate research in this topic, there is a need to collect real-world datasets in a principled manner.
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