1 citations · 1 across the 1 of their papers we have counts for
4 papers
Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds
Yuyang Zhao, Na Zhao, Gim Hee Lee
Semantic segmentation in 3D indoor scenes has achieved remarkable performance under the supervision of large-scale annotated data. However, previous works rely on the assumption th…
Few-shot 3D Point Cloud Semantic Segmentation
Na Zhao, Tat-Seng Chua, Gim Hee Lee
Many existing approaches for 3D point cloud semantic segmentation are fully supervised. These fully supervised approaches heavily rely on large amounts of labeled training data tha…
SESS: Self-Ensembling Semi-Supervised 3D Object Detection
Na Zhao, Tat-Seng Chua, Gim Hee Lee
The performance of existing point cloud-based 3D object detection methods heavily relies on large-scale high-quality 3D annotations. However, such annotations are often tedious and…
PS^2-Net: A Locally and Globally Aware Network for Point-Based Semantic Segmentation
Na Zhao, Tat-Seng Chua, Gim Hee Lee
In this paper, we present the PS^2-Net -- a locally and globally aware deep learning framework for semantic segmentation on 3D scene-level point clouds. In order to deeply incorpor…