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Mao Shan

7 accepted papers

2025

Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration

ICCV 2025poster

Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X datasets are limited in scope, diversity, and quality. To address these gaps, we present Mixed Signals, a comprehensive V2X…

Cited by 0SourcePDFScholar
2024

InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy Prediction

IROS 2024poster

This paper introduces InverseMatrixVT3D, an efficient method for transforming multi-view image features into 3D feature volumes for 3D semantic occupancy prediction. Existing methods for constructing 3D volumes often rely on depth estimation, device-specific operators, or transformer queries, which…

Cited by 14SourcecodeScholar
2023

Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection

ICRA 2023poster

Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are traine…

Cited by 20SourcecodeScholar
2022

See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation

RA-L 2022

Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D detectors trained for one lidar are tested on other types of lidars. Remarkable progress in lidar manufacturing has brou

Cited by 18SourcecodeScholar
2021

Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases

ICRA 2021poster

Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion both accurately and with minimal delay. Understanding the uncertainty of the prediction is also crucial. Most existing approaches however can only estimate uncertainty through repeated…

Cited by 37SourceScholar
2020

Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction Using a Graph Vehicle-Pedestrian Attention Network

RA-L 2020

Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between m

Cited by 88SourceScholar
2019

Uncertainty Estimation for Projecting Lidar Points onto Camera Images for Moving Platforms

ICRA 2019poster

Combining multiple sensors for advanced perception is a crucial requirement for autonomous vehicle navigation. Heterogeneous sensors are used to obtain rich information about the surrounding environment. The combination of the camera and lidar sensors enables precise range information that can be pr…

Cited by 7SourceScholar