2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2025
BEEP3D: Box-Supervised End-to-End Pseudo-Mask Generation for 3D Instance Segmentation
Youngju Yoo, Seho Kim, Changick Kim
3D instance segmentation is crucial for understanding complex 3D environments, yet fully supervised methods require dense point-level annotations, resulting in substantial annotati…
cs.CV2023★ 2 cited
DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection
Se-Ho Kim, Inyong Koo, Inyoung Lee +2
Denoising diffusion models show remarkable performances in generative tasks, and their potential applications in perception tasks are gaining interest. In this paper, we introduce…
cs.CV2023
PG-RCNN: Semantic Surface Point Generation for 3D Object Detection
Inyong Koo, Inyoung Lee, Se-Ho Kim +3
One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail to capture the complete spatial information about the objects due to long distance and…