I am a Ph.D. student at the Visualization and MultiMedia Lab of the University of Zurich, working as an Early-Stage Researcher in the H2020 MSCA-ITN project EVOCATION. The general topic is 3D reconstruction of indoor environments.
I am thrilled to be awarded the scholarship in the Rising Stars in Computer Graphics program (2022-2023), organized by WiGRAPH, an official group of the SIGGRAPH conference. I have also been awarded the GHC 2020 and 2022 scholarships to attend the largest gathering of women in technology, known as the Grace Hopper Celebration.
I earned my BSc degree in computer science from the National University of Saint Augustine, Arequipa - Peru, and my MSc degree in computer science from Federal Fluminense University, Niteroi - Brazil.
My research interests lie at the intersection of geometry processing, 3D computer vision, and deep learning for point clouds. In particular, I am interested in building learning systems that reconstruct structural elements and understand objects of indoor environments.
State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments
Creating high-level structured 3D models of real-world indoor scenes from captured data is a fundamental task which has important applications in many fields. Given the complexity and variability of interior environments and the need to cope with noisy and partial captured data, many open research problems remain, despite the substantial progress made in the past decade. In this survey, we provide an up-to-date integrative view of the field, bridging complementary views coming from computer graphics and computer vision. After providing a characterization of input sources, we define the structure of output models and the priors exploited to bridge the gap between imperfect sources and desired output. We then identify and discuss the main components of a structured reconstruction pipeline, and review how they are combined in scalable solutions working at the building level. We finally point out relevant research issues and analyze research trends.v