8 citations · 9 across the 8 of their papers we have counts for
8 papers · 1 filter
Breaking the Model Forgetting Cycle in Long-Incremental 3D Object Detection
Peisheng Qian, Jie Xu, Xulei Yang +1
Incremental 3D object detection requires a detector to learn novel object classes while remembering previously learned ones over sequentially arriving data. Previous methods, prima…
PanDA: Unsupervised Domain Adaptation for Multimodal 3D Panoptic Segmentation in Autonomous Driving
Yining Pan, Shijie Li, Yuchen Wu +2
This paper presents the first study on Unsupervised Domain Adaptation (UDA) for multimodal 3D panoptic segmentation (mm-3DPS), aiming to improve generalization under domain shifts…
Few-Shot Incremental 3D Object Detection in Dynamic Indoor Environments
Yun Zhu, Jianjun Qian, Jian Yang +2
Incremental 3D object perception is a critical step toward embodied intelligence in dynamic indoor environments. However, existing incremental 3D detection methods rely on extensiv…
SCOPE: Scene-Contextualized Incremental Few-Shot 3D Segmentation
Vishal Thengane, Zhaochong An, Tianjin Huang +5
Incremental Few-Shot (IFS) segmentation aims to learn new categories over time from only a few annotations. Although widely studied in 2D, it remains underexplored for 3D point clo…
Dual-supervised Asymmetric Co-training for Semi-supervised Medical Domain Generalization
Jincai Song, Haipeng Chen, Jun Qin +1
Semi-supervised domain generalization (SSDG) in medical image segmentation offers a promising solution for generalizing to unseen domains during testing, addressing domain shift ch…
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection
Jiangyi Wang, Na Zhao
Active learning has emerged as a promising approach to reduce the substantial annotation burden in 3D object detection tasks, spurring several initiatives in outdoor environments.…
On-the-fly Point Feature Representation for Point Clouds Analysis
Jiangyi Wang, Zhongyao Cheng, Na Zhao +2
Point cloud analysis is challenging due to its unique characteristics of unorderness, sparsity and irregularity. Prior works attempt to capture local relationships by convolution o…
Improving 3D Occupancy Prediction through Class-balancing Loss and Multi-scale Representation
Huizhou Chen, Jiangyi Wang, Yuxin Li +3
3D environment recognition is essential for autonomous driving systems, as autonomous vehicles require a comprehensive understanding of surrounding scenes. Recently, the predominan…