1 code implementation • 16 May 2024 • Yilun Chen, Shuai Yang, Haifeng Huang, Tai Wang, Ruiyuan Lyu, Runsen Xu, Dahua Lin, Jiangmiao Pang
Prior studies on 3D scene understanding have primarily developed specialized models for specific tasks or required task-specific fine-tuning.
1 code implementation • 26 Dec 2023 • Tai Wang, Xiaohan Mao, Chenming Zhu, Runsen Xu, Ruiyuan Lyu, Peisen Li, Xiao Chen, Wenwei Zhang, Kai Chen, Tianfan Xue, Xihui Liu, Cewu Lu, Dahua Lin, Jiangmiao Pang
In the realm of computer vision and robotics, embodied agents are expected to explore their environment and carry out human instructions.
3 code implementations • 31 Aug 2023 • Runsen Xu, Xiaolong Wang, Tai Wang, Yilun Chen, Jiangmiao Pang, Dahua Lin
The unprecedented advancements in Large Language Models (LLMs) have shown a profound impact on natural language processing but are yet to fully embrace the realm of 3D understanding.
Ranked #3 on 3D Object Captioning on Objaverse
1 code implementation • NeurIPS 2023 • Xiaolong Wang, Runsen Xu, Zuofan Cui, Zeyu Wan, Yu Zhang
In this paper, we introduce a novel approach to fine-grained cross-view geo-localization.
1 code implementation • CVPR 2023 • Runsen Xu, Tai Wang, Wenwei Zhang, Runjian Chen, Jinkun Cao, Jiangmiao Pang, Dahua Lin
This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object detection benchmark on the Waymo dataset.
1 code implementation • 8 Jun 2022 • Runjian Chen, Yao Mu, Runsen Xu, Wenqi Shao, Chenhan Jiang, Hang Xu, Zhenguo Li, Ping Luo
In this paper, we propose CO^3, namely Cooperative Contrastive Learning and Contextual Shape Prediction, to learn 3D representation for outdoor-scene point clouds in an unsupervised manner.
no code implementations • 7 Apr 2021 • Zhaoyang Huang, Xiaokun Pan, Runsen Xu, Yan Xu, Ka Chun Cheung, Guofeng Zhang, Hongsheng Li
However, local image contents are inevitably ambiguous and error-prone during the cross-image feature matching process, which hinders downstream tasks.