7 citations · 19 across the 7 of their papers we have counts for
7 papers
CVKD-UDA: Cross-View Knowledge Distillation for 3D Unsupervised Domain Adaptive Segmentation
Zhimin Yuan, Ming Cheng, Shangshu Yu +4
3D unsupervised domain adaptive (UDA) segmentation mitigates the high cost of manual annotations of the new domain data. Self-training has emerged as the dominant approach in this…
Density-guided Translator Boosts Synthetic-to-Real Unsupervised Domain Adaptive Segmentation of 3D Point Clouds
Zhimin Yuan, Wankang Zeng, Yanfei Su +4
3D synthetic-to-real unsupervised domain adaptive segmentation is crucial to annotating new domains. Self-training is a competitive approach for this task, but its performance is l…
Multi-Graph Fusion Networks for Urban Region Embedding
Shangbin Wu, Xu Yan, Xiaoliang Fan +5
Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime predict…
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Clouds
Yanfei Su, Weiquan Liu, Zhimin Yuan +4
Semantic segmentation of building facade is significant in various applications, such as urban building reconstruction and damage assessment. As there is a lack of 3D point clouds…
RF-Net: An End-to-End Image Matching Network based on Receptive Field
Xuelun Shen, Cheng Wang, Xin Li +5
This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images. Building end-to-end trainable mat…
LO-Net: Deep Real-time Lidar Odometry
Qing Li, Shaoyang Chen, Cheng Wang +4
We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations that go through indivi…