The method could also be deployed on drones or small robots. These devices often face challenges in capturing 3D groundtruth data, making a single-camera-based method highly advantageous. “It’s still quite heavy to train,” AnhQuan reveals. “It takes five days. The next step would be to make that more efficient. Also, inference is quite slow for now. Another step is to incorporate the semantic information inside the method. Currently, it only predicts the scene’s geometry without the semantic information. I hope other researchers will take it from here.” Anh-Quan is originally from Hanoi, the capital of Vietnam, which he tells us has a lot of “beautiful landscapes.” Outside of this project, his work is focused on 3D scene reconstruction from images or point clouds, aiming to reduce the reliance on supervision, estimate model uncertainty, and enhance explainability. “I will finish my PhD next year,” he tells us. “The next step is to find a job – I want to be a research scientist in a company.” To learn more about Anh-Quan’s work, visit his poster this morning at 10:30-12:30. 14 DAILY ICCV Thursday Poster Presentation
RkJQdWJsaXNoZXIy NTc3NzU=