{
  "@context": "http://schema.org",
  "@type": "Event",
  "name": "Synthetic Data Generation for 3D Mesh Prediction and Spatial Reasoning During Multi-Agent Robotic Missions",
  "startDate": "2021-01",
  "location": {
    "@type": "Place",
    "name": "AIAA SciTech Forum",
    "address": {
      "@type": "PostalAddress",
      "addressLocality": "Location of conference (if known)",
      "addressRegion": "Region of conference (if known)",
      "addressCountry": "Country of conference (if known)"
    }
  },
  "sponsor": {
    "@type": "Organization",
    "name": "American Institute of Aeronautics and Astronautics (AIAA)"
  },
  "performer": [
    {
      "@type": "Person",
      "name": "James Ecker"
    },
    {
      "@type": "Person",
      "name": "Benjamin Kelley"
    },
    {
      "@type": "Person",
      "name": "Danette Allen"
    }
  ],
  "workFeatured": [
    {
      "@type": "CreativeWork",
      "name": "Computer Vision During In-Space Assembly",
      "about": "Difficulties in space operations such as illumination, angle, orientation, movement, energy, and mass constraints."
    },
    {
      "@type": "CreativeWork",
      "name": "Mask R-CNN and Mesh R-CNN in Space Technology",
      "about": "Techniques for instance segmentation and 3D mesh prediction in space robotics."
    },
    {
      "@type": "CreativeWork",
      "name": "Synthesizing Data for Space Robotics",
      "about": "Using tools like Blender, ROS, Gazebo, and Mujoco for creating synthetic datasets for computer vision systems."
    }
  ],
  "about": {
    "@type": "Thing",
    "name": "Computer Vision and Robotics in Space",
    "description": "The presentation covers topics such as synthetic data generation, computer vision challenges in space, mitigation of resource constraints, and advancements in 3D mesh prediction models."
  },
  "keywords": [
    "Synthetic Data",
    "3D Mesh Prediction",
    "Spatial Reasoning",
    "Robotics",
    "Computer Vision",
    "In-Space Assembly",
    "AI",
    "Machine Learning",
    "Deep Learning",
    "GANs",
    "PointCloud Generation"
  ],
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Mask R-CNN",
      "author": [
        {
          "@type": "Person",
          "name": "Kaiming He"
        },
        {
          "@type": "Person",
          "name": "Georgia Gkioxari"
        },
        {
          "@type": "Person",
          "name": "Piotr Dollár"
        },
        {
          "@type": "Person",
          "name": "Ross Girshick"
        }
      ],
      "datePublished": "2017",
      "url": "https://doi.org/10.1109/ICCV.2017.322"
    },
    {
      "@type": "CreativeWork",
      "name": "Mesh R-CNN",
      "author": [
        {
          "@type": "Person",
          "name": "Georgia Gkioxari"
        },
        {
          "@type": "Person",
          "name": "Jitendra Malik"
        }
      ],
      "datePublished": "2019",
      "url": "https://doi.org/10.1109/ICCV.2019.00988"
    },
    {
      "@type": "CreativeWork",
      "name": "Assistive Relative Pose Estimation for On-orbit Assembly using Convolutional Neural Networks",
      "author": {
        "@type": "Person",
        "name": "S. D. Sonawani et al."
      },
      "datePublished": "2020",
      "url": "http://arxiv.org/abs/2001.10673"
    },
    {
      "@type": "CreativeWork",
      "name": "3D Point Cloud Generation from 2D Depth Camera Images using Successive Triangulation",
      "author": {
        "@type": "Person",
        "name": "B. Pal et al."
      },
      "datePublished": "2017",
      "url": "https://doi.org/10.1109/ICIMIA.2017.7975586"
    },
    {
      "@type": "CreativeWork",
      "name": "Learning Localized Representations of Point Clouds with Graph-Convolutional Generative Adversarial Networks",
      "author": {
        "@type": "Person",
        "name": "D. Valsesia et al."
      },
      "datePublished": "2019",
      "url": "https://ieeexplore.ieee.org/document/8642330"
    },
    {
      "@type": "CreativeWork",
      "name": "Spectral-GANs for High Resolution 3D Point Cloud Generation",
      "author": {
        "@type": "Person",
        "name": "S. Ramasinghe et al."
      },
      "datePublished": "2019",
      "url": "http://arxiv.org/abs/1912.01800"
    }
  ]
}