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MITO dataset and simulator advance non-line-of-sight perception with mmWave sensing

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MITO, a novel dataset and simulator designed to enhance non-line-of-sight (NLOS) perception using millimeter-wave (mmWave) sensing. This dataset comprises over 24 million mmWave frames and 550 high-resolution mmWave images, alongside RGB-D data and segmentation masks, collected using a UR5 robotic arm equipped with mmWave radars and a camera. MITO aims to overcome the limitations of optical perception by enabling systems to 'see' through common occlusions like cardboard or fabric, establishing benchmarks for NLOS segmentation and classification. AI

IMPACT Enables AI systems to perceive through occlusions, potentially improving robotics and autonomous systems.

RANK_REASON The cluster describes a new dataset and simulator for computer vision research, including benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

MITO dataset and simulator advance non-line-of-sight perception with mmWave sensing

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The cluster describes a new dataset and simulator for computer vision research, including benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Tara Boroushaki, Laura Dodds, Cusuh Ham, Fadel Adib ·

    MITO: A Millimeter-Wave Dataset and Simulator for Non-Line-of-Sight Perception

    arXiv:2502.10259v4 Announce Type: replace Abstract: The ability to observe the world is fundamental to reasoning and making informed decisions on how to interact with the environment. However, optical perception can often be disrupted due to common occurrences like occlusions, wh…

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