Adversarially masking synthetic to mimic real: Adaptive noise injection for point cloud segmentation adaptation

G Li, G Kang, X Wang, Y Wei… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
This paper considers the synthetic-to-real adaptation of point cloud semantic segmentation,
which aims to segment the real-world point clouds with only synthetic labels available …

Dg-pic: Domain generalized point-in-context learning for point cloud understanding

J Jiang, Q Zhou, Y Li, X Lu, M Wang, L Ma… - … on Computer Vision, 2025 - Springer
Recent point cloud understanding research suffers from performance drops on unseen data,
due to the distribution shifts across different domains. While recent studies use Domain …

Annotator: A generic active learning baseline for lidar semantic segmentation

B Xie, S Li, Q Guo, C Liu… - Advances in Neural …, 2023 - proceedings.neurips.cc
Active learning, a label-efficient paradigm, empowers models to interactively query an oracle
for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem …

Recent advances in multi-modal 3D scene understanding: A comprehensive survey and evaluation

Y Lei, Z Wang, F Chen, G Wang, P Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Multi-modal 3D scene understanding has gained considerable attention due to its wide
applications in many areas, such as autonomous driving and human-computer interaction …

Sira-pcr: Sim-to-real adaptation for 3d point cloud registration

S Chen, H Xu, R Li, G Liu, CW Fu… - Proceedings of the …, 2023 - openaccess.thecvf.com
Point cloud registration is essential for many applications. However, existing real datasets
require extremely tedious and costly annotations, yet may not provide accurate camera …

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

MH Tanveer, Z Fatima, S Zardari, D Guerra-Zubiaga - Applied Sciences, 2023 - mdpi.com
This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …

3d vision and language pretraining with large-scale synthetic data

D Yang, Z Xu, W Mo, Q Chen, S Huang… - arXiv preprint arXiv …, 2024 - arxiv.org
3D Vision-Language Pre-training (3D-VLP) aims to provide a pre-train model which can
bridge 3D scenes with natural language, which is an important technique for embodied …

Self-supervised latent feature learning for partial point clouds recognition

Z Zhang, F Da - Pattern Recognition Letters, 2023 - Elsevier
Abstract 3D vision perception, especially point clouds classification is fundamental and
popular in safety-critical systems such as autonomous driving and robotics automation …

Can 3D Vision-Language Models Truly Understand Natural Language?

W Deng, J Yang, R Ding, J Liu, Y Li, X Qi… - arXiv preprint arXiv …, 2024 - arxiv.org
Rapid advancements in 3D vision-language (3D-VL) tasks have opened up new avenues
for human interaction with embodied agents or robots using natural language. Despite this …

Construct to Associate: Cooperative Context Learning for Domain Adaptive Point Cloud Segmentation

G Li - Proceedings of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
This paper tackles the domain adaptation problem in point cloud semantic segmentation
which performs adaptation from a fully labeled domain (source domain) to an unlabeled …