no code implementations • 12 Dec 2023 • Yoonwoo Jeong, Jinwoo Lee, Chiheon Kim, Minsu Cho, Doyup Lee
Transfer learning of large-scale Text-to-Image (T2I) models has recently shown impressive potential for Novel View Synthesis (NVS) of diverse objects from a single image.
no code implementations • 20 Jun 2023 • SeungWook Kim, Chunghyun Park, Yoonwoo Jeong, Jaesik Park, Minsu Cho
Learning to predict reliable characteristic orientations of 3D point clouds is an important yet challenging problem, as different point clouds of the same class may have largely varying appearances.
1 code implementation • 24 Aug 2022 • Yoonwoo Jeong, Seungjoo Shin, Junha Lee, Christopher Choy, Animashree Anandkumar, Minsu Cho, Jaesik Park
The recent progress in implicit 3D representation, i. e., Neural Radiance Fields (NeRFs), has made accurate and photorealistic 3D reconstruction possible in a differentiable manner.
1 code implementation • 15 Jun 2022 • Jongmin Lee, Yoonwoo Jeong, Minsu Cho
We study the problem of learning to assign a characteristic pose, i. e., scale and orientation, for an image region of interest.
1 code implementation • CVPR 2022 • Chunghyun Park, Yoonwoo Jeong, Minsu Cho, Jaesik Park
The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem.
Ranked #24 on Semantic Segmentation on S3DIS
no code implementations • 29 Sep 2021 • Chunghyun Park, Yoonwoo Jeong, Minsu Cho, Jaesik Park
Although sparse convolution is efficient and scalable for large 3D scenes, the quantization artifacts impair geometric details and degrade prediction accuracy.
1 code implementation • ICCV 2021 • Yoonwoo Jeong, Seokjun Ahn, Christopher Choy, Animashree Anandkumar, Minsu Cho, Jaesik Park
We also propose a new geometric loss function, viz., projected ray distance loss, to incorporate geometric consistency for complex non-linear camera models.