Search Results for author: Lingdong Kong

Found 26 papers, 21 papers with code

Is Your HD Map Constructor Reliable under Sensor Corruptions?

no code implementations18 Jun 2024 Xiaoshuai Hao, Mengchuan Wei, Yifan Yang, Haimei Zhao, HUI ZHANG, Yi Zhou, Qiang Wang, Weiming Li, Lingdong Kong, Jing Zhang

These insights provide a pathway for developing more reliable HD map construction methods, which are essential for the advancement of autonomous driving technology.

Autonomous Driving Data Augmentation

An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models

1 code implementation23 May 2024 Jiahao Sun, Chunmei Qing, Xiang Xu, Lingdong Kong, Youquan Liu, Li Li, Chenming Zhu, Jingwei Zhang, Zeqi Xiao, Runnan Chen, Tai Wang, Wenwei Zhang, Kai Chen

In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments.

Autonomous Driving Benchmarking +3

Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving

1 code implementation8 May 2024 Lingdong Kong, Xiang Xu, Jiawei Ren, Wenwei Zhang, Liang Pan, Kai Chen, Wei Tsang Ooi, Ziwei Liu

Efficient data utilization is crucial for advancing 3D scene understanding in autonomous driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully supervised methods.

Autonomous Driving LIDAR Semantic Segmentation +2

Multi-Space Alignments Towards Universal LiDAR Segmentation

1 code implementation CVPR 2024 Youquan Liu, Lingdong Kong, Xiaoyang Wu, Runnan Chen, Xin Li, Liang Pan, Ziwei Liu, Yuexin Ma

A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception.

Autonomous Driving Segmentation

Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding

1 code implementation25 Mar 2024 Lingdong Kong, Xiang Xu, Jun Cen, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu

Safety-critical 3D scene understanding tasks necessitate not only accurate but also confident predictions from 3D perception models.

Data Augmentation Scene Understanding

Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

1 code implementation15 Mar 2024 Jingyi Xu, Weidong Yang, Lingdong Kong, Youquan Liu, Rui Zhang, Qingyuan Zhou, Ben Fei

Then, another VFM trained on fine-grained 2D masks is adopted to guide the generation of semantically augmented images and point clouds to enhance the performance of neural networks, which mix the data from source and target domains like view frustums (FrustumMixing).

3D Semantic Segmentation Autonomous Driving +2

Learning to Adapt SAM for Segmenting Cross-domain Point Clouds

no code implementations13 Oct 2023 Xidong Peng, Runnan Chen, Feng Qiao, Lingdong Kong, Youquan Liu, Tai Wang, Xinge Zhu, Yuexin Ma

Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data.

General Knowledge Image Segmentation +4

RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions

1 code implementation13 Apr 2023 Shaoyuan Xie, Lingdong Kong, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, Ziwei Liu

Our experiments further demonstrate that pre-training and depth-free BEV transformation has the potential to enhance out-of-distribution robustness.

Robust Camera Only 3D Object Detection

Rethinking Range View Representation for LiDAR Segmentation

no code implementations ICCV 2023 Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, Ziwei Liu

We show that, for the first time, a range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks, i. e., SemanticKITTI, nuScenes, and ScribbleKITTI.

3D Semantic Segmentation Autonomous Driving +4

CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP

1 code implementation CVPR 2023 Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, Wenping Wang

For the first time, our pre-trained network achieves annotation-free 3D semantic segmentation with 20. 8% and 25. 08% mIoU on nuScenes and ScanNet, respectively.

3D Semantic Segmentation Contrastive Learning +4

Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective

1 code implementation NeurIPS 2023 Pengfei Wei, Lingdong Kong, Xinghua Qu, Yi Ren, Zhiqiang Xu, Jing Jiang, Xiang Yin

Specifically, we consider the generation of cross-domain videos from two sets of latent factors, one encoding the static information and another encoding the dynamic information.

Action Recognition Disentanglement +1

ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation

1 code implementation30 Nov 2021 Lingdong Kong, Niamul Quader, Venice Erin Liong

We present ConDA, a concatenation-based domain adaptation framework for LiDAR segmentation that: 1) constructs an intermediate domain consisting of fine-grained interchange signals from both source and target domains without destabilizing the semantic coherency of objects and background around the ego-vehicle; and 2) utilizes the intermediate domain for self-training.

Autonomous Driving LIDAR Semantic Segmentation +2

Free Lunch for Co-Saliency Detection: Context Adjustment

no code implementations4 Aug 2021 Lingdong Kong, Prakhar Ganesh, Tan Wang, Junhao Liu, Le Zhang, Yao Chen

We hope that the scale, diversity, and quality of our dataset can benefit researchers in this area and beyond.

counterfactual Saliency Detection +1

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