Search Results for author: Beipeng Mu

Found 5 papers, 3 papers with code

GLRD: Global-Local Collaborative Reason and Debate with PSL for 3D Open-Vocabulary Detection

no code implementations26 Mar 2025 Xingyu Peng, Si Liu, Chen Gao, Yan Bai, Beipeng Mu, Xiaofei Wang, Huaxia Xia

To further boost the LLM's ability of precise decisions, we also design a probabilistic soft logic solver (OV-PSL) to search for the optimal solution, and a debate scheme to confirm the class of confusable objects.

Common Sense Reasoning Object +1

Global-Local Collaborative Inference with LLM for Lidar-Based Open-Vocabulary Detection

1 code implementation12 Jul 2024 Xingyu Peng, Yan Bai, Chen Gao, Lirong Yang, Fei Xia, Beipeng Mu, Xiaofei Wang, Si Liu

In this paper, we propose a Global-Local Collaborative Scheme (GLIS) for the lidar-based OVD task, which contains a local branch to generate object-level detection result and a global branch to obtain scene-level global feature.

Collaborative Inference Language Modelling +3

Eliminating Cross-modal Conflicts in BEV Space for LiDAR-Camera 3D Object Detection

no code implementations12 Mar 2024 Jiahui Fu, Chen Gao, Zitian Wang, Lirong Yang, Xiaofei Wang, Beipeng Mu, Si Liu

Recent 3D object detectors typically utilize multi-sensor data and unify multi-modal features in the shared bird's-eye view (BEV) representation space.

3D Object Detection object-detection

Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future

2 code implementations6 Dec 2023 Hongyang Li, Yang Li, Huijie Wang, Jia Zeng, Huilin Xu, Pinlong Cai, Li Chen, Junchi Yan, Feng Xu, Lu Xiong, Jingdong Wang, Futang Zhu, Chunjing Xu, Tiancai Wang, Fei Xia, Beipeng Mu, Zhihui Peng, Dahua Lin, Yu Qiao

With the continuous maturation and application of autonomous driving technology, a systematic examination of open-source autonomous driving datasets becomes instrumental in fostering the robust evolution of the industry ecosystem.

Autonomous Driving

SLAM with Objects using a Nonparametric Pose Graph

2 code implementations19 Apr 2017 Beipeng Mu, Shih-Yuan Liu, Liam Paull, John Leonard, Jonathan How

The \textit{data association} and \textit{simultaneous localization and mapping} (SLAM) problems are, individually, well-studied in the literature.

Simultaneous Localization and Mapping

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