@InProceedings{Freitas:2023:ViAnTo,
               author = "Freitas, Pedro S. de",
          affiliation = "Federal University of Rio Grande do Sul, SENAI Innovation 
                         Institute for Integrated Solutions in Metalmechanics",
                title = "REIS: A Visual Analytics Tool for Rendering and Exploring Instance 
                         Segmentation of Point Clouds",
            booktitle = "Proceedings...",
                 year = "2023",
               editor = "Clua, Esteban Walter Gonzalez and K{\"o}rting, Thales Sehn and 
                         Paulovich, Fernando Vieira and Feris, Rogerio",
         organization = "Conference on Graphics, Patterns and Images, 36. (SIBGRAPI)",
             keywords = "point cloud segmentation, visualization.",
             abstract = "3D Instance Segmentation (3DIS) of Point Clouds (PCs) is valuable 
                         for applications like autonomous vehicles, robotics, and Building 
                         Information Modeling (BIM). Current work on this topic is guided 
                         mainly by global metrics like mAP, which arguably do not support a 
                         deep, informed analysis of technique tradeoffs and, more 
                         importantly, directions for improvement. Qualitative analysis is 
                         widely adopted to provide such guidance, but it is generally 
                         implemented ad-hoc. This is true across many tasks in Deep 
                         Learning, but PC 3DIS is especially challenging to visually 
                         analyze due to the many variables involved: three spatial 
                         dimensions, colors, semantic labels, and instance IDs. We propose 
                         REIS, a visual analytics tool for Rendering and Exploring Instance 
                         Segmentation results. It supports qualitative analysis in two 
                         ways: first, through PC renderings targeted at efficient 
                         investigation of 3DIS results; second, by providing a systematic 
                         way to explore these results via the interactive Instance 
                         Detection Matrix- a confusion matrix analog that summarizes error 
                         and success cases, and allows the user to navigate through them. 
                         To show the efficacy of REIS, we use it to evaluate a 
                         state-of-the-art 3DIS approach on the S3DIS dataset. Our code is 
                         available at https://github.com/pedrosidra/pcloud explorer.",
  conference-location = "Rio Grande, RS",
      conference-year = "Nov. 06-09, 2023",
                  doi = "10.1109/SIBGRAPI59091.2023.10347129",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI59091.2023.10347129",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/49LD4E2",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/49LD4E2",
           targetfile = "70_nocopyright.pdf",
        urlaccessdate = "2024, Apr. 29"
}