Sunghwan Hong

GenRec achieves unparalleled reconstruction fidelity while enhancing perceptual quality in novel view synthesis by intelligently separating reconstruction from generation.

Monocular 3D object detection can achieve robust performance without the pitfalls of 2D-to-3D lifting, thanks to a novel integration of metric reconstruction and detection.

LIME turns ordinary egocentric video into a powerful tool for robots to dynamically adjust their camera poses based on user intent, revolutionizing how we think about robotic perception.

GAM revolutionizes robot policy learning by seamlessly integrating 3D geometric reasoning, outperforming traditional models in accuracy and efficiency.

Achieving state-of-the-art registration accuracy without any learned parameters or depth sensors, PROSE redefines the landscape of egocentric scene understanding.

Achieving six times fewer Gaussians while surpassing state-of-the-art performance redefines efficiency in 3D scene reconstruction.

Forget training wheels: this training-free method leverages uncertainty to guide vision-language models to the right image regions, boosting performance on detail-oriented tasks.

Forget sparse annotations: a new dataset and compact VLM show that dense, complete screen parsing supervision unlocks substantial gains in UI understanding and grounding, even for large foundation models.