no code implementations • 9 Jun 2023 • Dong-Jae Lee, Jae Young Lee, Hyounguk Shon, Eojindl Yi, Yeong-Hun Park, Sung-Sik Cho, Junmo Kim
While most lightweight monocular depth estimation methods have been developed using convolution neural networks, the Transformer has been gradually utilized in monocular depth estimation recently.
no code implementations • 23 May 2022 • Eojindl Yi, JuYoung Yang, Junmo Kim
We evaluate the performance of our method on the recently proposed LiDAR segmentation UDA scenarios.
1 code implementation • 4 Feb 2022 • Pyunghwan Ahn, JuYoung Yang, Eojindl Yi, Chanho Lee, Junmo Kim
Point branch consists of MLPs, while projection branch transforms point features into a 2D feature map and then apply 2D convolutions.
1 code implementation • 1 Jan 2021 • Minju Jung, Hyounguk Shon, Eojindl Yi, SungHyun Baek, Junmo Kim
For the pruning and retraining phase, whether the pruned-and-retrained network benefits from the pretrained network indded is examined.
no code implementations • 2 Nov 2020 • JuYoung Yang, Chanho Lee, Pyunghwan Ahn, Haeil Lee, Eojindl Yi, Junmo Kim
In this paper, we propose a simple and efficient architecture named point projection and back-projection network (PBP-Net), which leverages 2D CNNs for the 3D point cloud segmentation.