Search Results for author: Xieyuanli Chen

Found 22 papers, 17 papers with code

Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM

no code implementations15 Sep 2023 Chenghao Shi, Xieyuanli Chen, Junhao Xiao, Bin Dai, Huimin Lu

In the end, we integrate our LCR-Net into a SLAM system and achieve robust and accurate online LiDAR SLAM in outdoor driving environments.

Point Cloud Registration Pose Estimation +1

PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View

1 code implementation19 Jun 2023 Peizheng Li, Shuxiao Ding, Xieyuanli Chen, Niklas Hanselmann, Marius Cordts, Juergen Gall

Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic.

Autonomous Driving motion prediction +1

RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving

no code implementations31 Mar 2023 Chenghao Shi, Xieyuanli Chen, Huimin Lu, Wenbang Deng, Junhao Xiao, Bin Dai

The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds' contextual and geometric information to generate robust superpoint correspondences.

Autonomous Driving Point Cloud Registration +1

CCL: Continual Contrastive Learning for LiDAR Place Recognition

1 code implementation24 Mar 2023 Jiafeng Cui, Xieyuanli Chen

The experimental results show that our CCL consistently improves the performance of different methods in different environments outperforming the state-of-the-art continual learning method.

Autonomous Driving Continual Learning +3

NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping

1 code implementation ICCV 2023 Junyuan Deng, Xieyuanli Chen, Songpengcheng Xia, Zhen Sun, Guoqing Liu, Wenxian Yu, Ling Pei

To bridge this gap, in this paper, we propose a novel NeRF-based LiDAR odometry and mapping approach, NeRF-LOAM, consisting of three modules neural odometry, neural mapping, and mesh reconstruction.

ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data

1 code implementation8 Mar 2023 Wenbang Deng, Kaihong Huang, Qinghua Yu, Huimin Lu, Zhiqiang Zheng, Xieyuanli Chen

In this paper, we present a flexible and effective OIS framework for LiDAR point cloud that can accurately segment both known and unknown instances (i. e., seen and unseen instance categories during training).

Autonomous Navigation Clustering +2

InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data

1 code implementation7 Mar 2023 Neng Wang, Chenghao Shi, Ruibin Guo, Huimin Lu, Zhiqiang Zheng, Xieyuanli Chen

We evaluated our approach on the LiDAR-MOS benchmark based on SemanticKITTI and achieved better moving object segmentation performance compared to state-of-the-art methods, demonstrating the effectiveness of our approach in integrating instance information for moving object segmentation.

Autonomous Navigation Semantic Segmentation +1

CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data

1 code implementation3 Feb 2023 Junyi Ma, Guangming Xiong, Jingyi Xu, Xieyuanli Chen

LiDAR-based place recognition (LPR) is one of the most crucial components of autonomous vehicles to identify previously visited places in GPS-denied environments.

Autonomous Vehicles

Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving

1 code implementation CVPR 2023 Lucas Nunes, Louis Wiesmann, Rodrigo Marcuzzi, Xieyuanli Chen, Jens Behley, Cyrill Stachniss

Especially in autonomous driving, point clouds are sparse, and objects appearance depends on its distance from the sensor, making it harder to acquire large amounts of labeled training data.

Autonomous Driving Panoptic Segmentation +1

SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation

1 code implementation28 Nov 2022 Hao Dong, Xianjing Zhang, Jintao Xu, Rui Ai, Weihao Gu, Huimin Lu, Juho Kannala, Xieyuanli Chen

However, current works are based on raw data or network feature-level fusion and only consider short-range HD map generation, limiting their deployment to realistic autonomous driving applications.

Autonomous Driving Depth Estimation

IR-MCL: Implicit Representation-Based Online Global Localization

1 code implementation6 Oct 2022 Haofei Kuang, Xieyuanli Chen, Tiziano Guadagnino, Nicky Zimmerman, Jens Behley, Cyrill Stachniss

The experiments suggest that the presented implicit representation is able to predict more accurate 2D LiDAR scans leading to an improved observation model for our particle filter-based localization.

Robot Navigation

Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors

1 code implementation27 Sep 2022 Hao Dong, Xieyuanli Chen, Mihai Dusmanu, Viktor Larsson, Marc Pollefeys, Cyrill Stachniss

A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization.

Dimensionality Reduction Image Retrieval +2

SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data

1 code implementation16 Sep 2022 Junyi Ma, Xieyuanli Chen, Jingyi Xu, Guangming Xiong

It uses multi-scale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion.

Autonomous Vehicles

BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM

1 code implementation15 Aug 2022 Yunge Cui, Xieyuanli Chen, Yinlong Zhang, Jiahua Dong, Qingxiao Wu, Feng Zhu

To address this limitation, we present a novel Bag of Words for real-time loop closing in 3D LiDAR SLAM, called BoW3D.

Simultaneous Localization and Mapping

Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

1 code implementation15 Aug 2022 Hao Dong, Xieyuanli Chen, Simo Särkkä, Cyrill Stachniss

We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation.

Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation

1 code implementation5 Jul 2022 Jiadai Sun, Yuchao Dai, Xianjing Zhang, Jintao Xu, Rui Ai, Weihao Gu, Xieyuanli Chen

We also use a point refinement module via 3D sparse convolution to fuse the information from both LiDAR range image and point cloud representations and reduce the artifacts on the borders of the objects.

Autonomous Driving Semantic Segmentation

Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod

no code implementations22 Apr 2022 Si Yang, Lihua Zheng, Xieyuanli Chen, Laura Zabawa, Man Zhang, Minjuan Wang

In the first step, we finetune an instance segmentation network pretrained by a source domain (MS COCO dataset) with a synthetic target domain (in-vitro soybean pods dataset).

Image Generation Instance Segmentation +2

Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks

1 code implementation28 Sep 2021 Benedikt Mersch, Xieyuanli Chen, Jens Behley, Cyrill Stachniss

In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans.

Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform

2 code implementations20 Aug 2020 Shijie Li, Xieyuanli Chen, Yun Liu, Dengxin Dai, Cyrill Stachniss, Juergen Gall

Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources.

Autonomous Vehicles Real-Time 3D Semantic Segmentation +1

Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer

no code implementations9 Sep 2019 Yucai Bai, Qin Zou, Xieyuanli Chen, Lingxi Li, Zhengming Ding, Long Chen

Given the fact that one same activity may be represented by videos in both high resolution (HR) and extreme low resolution (eLR), it is worth studying to utilize the relevant HR data to improve the eLR activity recognition.

Activity Recognition Privacy Preserving +1

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