1 code implementation • 22 Mar 2024 • Junbo Yin, Jianbing Shen, Runnan Chen, Wei Li, Ruigang Yang, Pascal Frossard, Wenguan Wang
HSF applies Point-to-Grid and Grid-to-Region transformers to capture the multimodal scene context at different granularities.
1 code implementation • 25 Dec 2023 • Li Xiang, Junbo Yin, Wei Li, Cheng-Zhong Xu, Ruigang Yang, Jianbing Shen
Specifically, DMA builds a domain-mixing 3D instance bank for the teacher and student models during training, resulting in aligned data representation.
1 code implementation • ICCV 2023 • Wencheng Han, Junbo Yin, Jianbing Shen
To bridge this gap, we propose a new Direction-aware Cumulative Convolution Network (DaCCN), which improves the depth feature representation in two aspects.
Monocular Depth Estimation Unsupervised Monocular Depth Estimation
no code implementations • 25 May 2023 • Wenhao Cheng, Junbo Yin, Wei Li, Ruigang Yang, Jianbing Shen
In this work, we propose a new multi-modal visual grounding task, termed LiDAR Grounding.
1 code implementation • 7 Dec 2022 • Xiang Li, Junbo Yin, Botian Shi, Yikang Li, Ruigang Yang, Jianbing Shen
In this paper, we present a more artful framework, LiDAR-guided Weakly Supervised Instance Segmentation (LWSIS), which leverages the off-the-shelf 3D data, i. e., Point Cloud, together with the 3D boxes, as natural weak supervisions for training the 2D image instance segmentation models.
1 code implementation • 6 Dec 2022 • Yan Wang, Junbo Yin, Wei Li, Pascal Frossard, Ruigang Yang, Jianbing Shen
However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e. g., from Waymo (64-beam) to nuScenes (32-beam).
1 code implementation • 26 Jul 2022 • Junbo Yin, Jin Fang, Dingfu Zhou, Liangjun Zhang, Cheng-Zhong Xu, Jianbing Shen, Wenguan Wang
To reduce the dependence on large supervision, semi-supervised learning (SSL) based approaches have been proposed.
no code implementations • 26 Jul 2022 • Junbo Yin, Jianbing Shen, Xin Gao, David Crandall, Ruigang Yang
In this paper, we propose to detect 3D objects by exploiting temporal information in multiple frames, i. e., the point cloud videos.
1 code implementation • 26 Jul 2022 • Junbo Yin, Dingfu Zhou, Liangjun Zhang, Jin Fang, Cheng-Zhong Xu, Jianbing Shen, Wenguan Wang
Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination.
1 code implementation • 23 Jun 2021 • Shaoqing Xu, Dingfu Zhou, Jin Fang, Junbo Yin, Zhou Bin, Liangjun Zhang
Then the segmentation results from different sensors are adaptively fused based on the proposed attention-based semantic fusion module.
no code implementations • 5 Mar 2021 • Dingfu Zhou, Xibin Song, Yuchao Dai, Junbo Yin, Feixiang Lu, Jin Fang, Miao Liao, Liangjun Zhang
3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed.
Ranked #19 on Monocular 3D Object Detection on KITTI Cars Moderate
1 code implementation • CVPR 2020 • Junbo Yin, Jianbing Shen, Chenye Guan, Dingfu Zhou, Ruigang Yang
In this paper, we propose an end-to-end online 3D video object detector that operates on point cloud sequences.
1 code implementation • CVPR 2020 • Junbo Yin, Wenguan Wang, Qinghao Meng, Ruigang Yang, Jianbing Shen
In this paper, we propose a novel MOT framework that unifies object motion and affinity model into a single network, named UMA, in order to learn a compact feature that is discriminative for both object motion and affinity measure.
1 code implementation • 11 Aug 2019 • Dingfu Zhou, Jin Fang, Xibin Song, Chenye Guan, Junbo Yin, Yuchao Dai, Ruigang Yang
In 2D/3D object detection task, Intersection-over-Union (IoU) has been widely employed as an evaluation metric to evaluate the performance of different detectors in the testing stage.