Abstract Reconstructing object deformation from a single image remains a significant challenge in computer vision and graphics. Existing methods typically rely on multi-view video to recover deformation, limiting their applicability under c... Read More
Abstract Understanding 3D scenes semantically and spatially is crucial for the safe navigation of robots and autonomous vehicles, aiding obstacle avoidance and accurate trajectory planning. Camera-based 3D semantic occupancy prediction, whi... Read More
Abstract We tackle animatable 3D dog reconstruction from a single image, noting the overlooked potential of animals. Particularly, we focus on dogs, emphasizing their intrinsic characteristics that complicate 3D observation. First, the cons... Read More
Innovative technologies that enhance robots’ visual and cognitive capabilities were showcased at the 2025 IEEE International Conference on Robotics and Automation (ICRA), the world’s foremost conference dedicated to advancing th... Read More
Abstract We propose a novel concept of dual and integrated latent topologies (DITTO in short) for implicit 3D reconstruction from noisy and sparse point clouds. Most existing methods predominantly focus on single latent type, such as point... Read More
![[UNIST Webinar] AI enabled Computational Imaging for Biomedical Applications](https://news.unist.ac.kr/wp-content/uploads/2022/10/Session-13.18-296x166.jpg)