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Pengpeng Liang

7 accepted papers

2026

Multi-Modal Assistance for Unsupervised Domain Adaptation on Point Cloud 3D Object Detection

AAAI 2026technical

Unsupervised domain adaptation for LiDAR-based 3D object detection (3D UDA) based on the teacher-student architecture with pseudo labels has achieved notable improvements in recent years. Although it is quite popular to collect point clouds and images simultaneously, little attention has been paid t

Cited by 0SourcePDFScholar
2024

Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors

CVPR 2024poster

Multi-camera-based 3D object detection has made notable progress in the past several years. However we observe that there are cases (e.g. faraway regions) in which popular 2D object detectors are more reliable than state-of-the-art 3D detectors. In this paper to improve the performance of query-base…

2023

CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention

ICRA 2023poster

3D lane detection is an integral part of au-tonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as input to predict 3D lanes. Such approaches require…

Cited by 60SourceScholar
2023

Multi-Correlation Siamese Transformer Network With Dense Connection for 3D Single Object Tracking

RA-L 2023

Point cloud-based 3D object tracking is an important task in autonomous driving. Though great advances regarding Siamese-based 3D tracking have been made recently, it remains challenging to learn the correlation between the template and search branches effectively with the sparse LIDAR point cloud d

Cited by 10SourcecodeScholar
2022

Traffic Context Aware Data Augmentation for Rare Object Detection in Autonomous Driving

ICRA 2022poster

Detection of rare objects (e.g., traffic cones, traffic barrels and traffic warning triangles) is an important perception task to improve the safety of autonomous driving. Training of such models typically requires a large number of annotated data which is expensive and time consuming to obtain. To…

Cited by 12SourcecodeScholar
2021

Coarse-to-fine Semantic Localization with HD Map for Autonomous Driving in Structural Scenes

IROS 2021poster

Robust and accurate localization is an essential component for robotic navigation and autonomous driving. The use of cameras for localization with high definition map (HD Map) provides an affordable localization sensor set. Existing methods suffer from pose estimation failure due to error prone data…

Cited by 37SourceScholar
2018

Planar Object Tracking in the Wild: A Benchmark

ICRA 2018poster

Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this pa…

Cited by 66SourceScholar