215 citations · 335 across the 26 of their papers we have counts for
27 papers
Towards Knowledge-driven Autonomous Driving
Xin Li, Yeqi Bai, Pinlong Cai +14
This paper explores the emerging knowledge-driven autonomous driving technologies. Our investigation highlights the limitations of current autonomous driving systems, in particular…
Optimizing the Placement of Roadside LiDARs for Autonomous Driving
Wentao Jiang, Hao Xiang, Xinyu Cai +5
Multi-agent cooperative perception is an increasingly popular topic in the field of autonomous driving, where roadside LiDARs play an essential role. However, how to optimize the p…
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Youquan Liu, Runnan Chen, Xin Li +9
Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a n…
StreetSurf: Extending Multi-view Implicit Surface Reconstruction to Street Views
Jianfei Guo, Nianchen Deng, Xinyang Li +6
We present a novel multi-view implicit surface reconstruction technique, termed StreetSurf, that is readily applicable to street view images in widely-used autonomous driving datas…
Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment Anything
Zhaotong Luo, Guohang Yan, Yikang Li
The research on extrinsic calibration between Light Detection and Ranging(LiDAR) and camera are being promoted to a more accurate, automatic and generic manner. Since deep learning…
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
Tao Ma, Xuemeng Yang, Hongbin Zhou +9
Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D…
AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
Jiakang Yuan, Bo Zhang, Xiangchao Yan +4
It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that c…
Automatic Surround Camera Calibration Method in Road Scene for Self-driving Car
Jixiang Li, Jiahao Pi, Guohang Yan +1
With the development of autonomous driving technology, sensor calibration has become a key technology to achieve accurate perception fusion and localization. Accurate calibration o…
SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification
Siyuan Huang, Bo Zhang, Botian Shi +3
Although Domain Generalization (DG) problem has been fast-growing in the 2D image tasks, its exploration on 3D point cloud data is still insufficient and challenged by more complex…
Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection
Bo Zhang, Jiakang Yuan, Botian Shi +3
Current 3D object detection models follow a single dataset-specific training and testing paradigm, which often faces a serious detection accuracy drop when they are directly deploy…
Perception Imitation: Towards Synthesis-free Simulator for Autonomous Vehicles
Xiaoliang Ju, Yiyang Sun, Yiming Hao +3
We propose a perception imitation method to simulate results of a certain perception model, and discuss a new heuristic route of autonomous driving simulator without data synthesis…
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion
Xin Li, Tao Ma, Yuenan Hou +8
LiDAR-camera fusion methods have shown impressive performance in 3D object detection. Recent advanced multi-modal methods mainly perform global fusion, where image features and poi…
SCPNet: Semantic Scene Completion on Point Cloud
Zhaoyang Xia, Youquan Liu, Xin Li +5
Training deep models for semantic scene completion (SSC) is challenging due to the sparse and incomplete input, a large quantity of objects of diverse scales as well as the inheren…
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection
Jiakang Yuan, Bo Zhang, Xiangchao Yan +4
Unsupervised Domain Adaptation (UDA) technique has been explored in 3D cross-domain tasks recently. Though preliminary progress has been made, the performance gap between the UDA-b…
Rethinking Range View Representation for LiDAR Segmentation
Lingdong Kong, Youquan Liu, Runnan Chen +6
LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point- or voxel-based methods as they often yield better performance than the traditional range…
LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Driving
Xiang Li, Junbo Yin, Botian Shi +3
Image instance segmentation is a fundamental research topic in autonomous driving, which is crucial for scene understanding and road safety. Advanced learning-based approaches ofte…
SensorX2car: Sensors-to-car calibration for autonomous driving in road scenarios
Guohang Yan, Zhaotong Luo, Zhuochun Liu +1
Properly-calibrated sensors are the prerequisite for a dependable autonomous driving system. However, most prior methods focus on extrinsic calibration between sensors, and few foc…
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
Runnan Chen, Youquan Liu, Lingdong Kong +6
Contrastive Language-Image Pre-training (CLIP) achieves promising results in 2D zero-shot and few-shot learning. Despite the impressive performance in 2D, applying CLIP to help the…
UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation Pipeline
Ben Fei, Siyuan Huang, Jiakang Yuan +5
State-of-the-art 3D semantic segmentation models are trained on off-the-shelf public benchmarks, but they will inevitably face the challenge of recognition accuracy drop when these…
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
Xinyu Cai, Wentao Jiang, Runsheng Xu +4
Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention. Infrastructure sensors play a critical role in this research field; however, how to…
Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection
Xin Li, Botian Shi, Yuenan Hou +4
Multi-modal 3D object detection has been an active research topic in autonomous driving. Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D…
Online LiDAR-Camera Extrinsic Parameters Self-checking
Pengjin Wei, Guohang Yan, Yikang Li +3
With the development of neural networks and the increasing popularity of automatic driving, the calibration of the LiDAR and the camera has attracted more and more attention. This…
Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation
Yuenan Hou, Xinge Zhu, Yuexin Ma +2
This article addresses the problem of distilling knowledge from a large teacher model to a slim student network for LiDAR semantic segmentation. Directly employing previous distill…
Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis
Ben Fei, Weidong Yang, Wenming Chen +5
Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress…