no code implementations • 9 Apr 2024 • Minh-Quan Dao, Holger Caesar, Julie Stephany Berrio, Mao Shan, Stewart Worrall, Vincent Frémont, Ezio Malis
We address this challenge by devising a label-efficient object detection method for RSU based on unsupervised object discovery.
no code implementations • 3 Mar 2024 • Zhenxing Ming, Julie Stephany Berrio, Mao Shan, Stewart Worrall
A comprehensive understanding of 3D scenes is crucial in autonomous vehicles (AVs), and recent models for 3D semantic occupancy prediction have successfully addressed the challenge of describing real-world objects with varied shapes and classes.
1 code implementation • 23 Jan 2024 • Zhenxing Ming, Julie Stephany Berrio, Mao Shan, Stewart Worrall
In contrast, our approach leverages two projection matrices to store the static mapping relationships and matrix multiplications to efficiently generate global Bird's Eye View (BEV) features and local 3D feature volumes.
no code implementations • 17 Oct 2023 • Santiago Gerling Konrad, Julie Stephany Berrio, Mao Shan, Favio Masson, Stewart Worrall
This paper introduces a dual-source approach integrating data from an infrared camera facing the vehicle operator and vehicle perception systems to produce a metric for driver alertness in order to promote and ensure safe operator behaviour.
1 code implementation • 11 Aug 2023 • Darren Tsai, Julie Stephany Berrio, Mao Shan, Eduardo Nebot, Stewart Worrall
MS3D++ provides a straightforward approach to domain adaptation by generating high-quality pseudo-labels, enabling the adaptation of 3D detectors to a diverse range of lidar types, regardless of their density.
1 code implementation • 14 Jul 2023 • Zhenxing Ming, Julie Stephany Berrio, Mao Shan, Eduardo Nebot, Stewart Worrall
For smart vehicles driving through signalised intersections, it is crucial to determine whether the vehicle has right of way given the state of the traffic lights.
1 code implementation • 4 Jul 2023 • Minh-Quan Dao, Julie Stephany Berrio, Vincent Frémont, Mao Shan, Elwan Héry, Stewart Worrall
In this work, we devise a simple yet effective collaboration method that achieves a better bandwidth-performance tradeoff than prior state-of-the-art methods while minimizing changes made to the single-vehicle detection models and relaxing unrealistic assumptions on inter-agent synchronization.
1 code implementation • 5 Apr 2023 • Darren Tsai, Julie Stephany Berrio, Mao Shan, Eduardo Nebot, Stewart Worrall
Our proposed Kernel-Density Estimation (KDE) Box Fusion method fuses box proposals from multiple domains to obtain pseudo-labels that surpass the performance of the best source domain detectors.
1 code implementation • 14 Sep 2022 • Darren Tsai, Julie Stephany Berrio, Mao Shan, Eduardo Nebot, Stewart Worrall
With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns.
1 code implementation • 17 Nov 2021 • Darren Tsai, Julie Stephany Berrio, Mao Shan, Stewart Worrall, Eduardo Nebot
Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects.
no code implementations • 9 Jul 2020 • Julie Stephany Berrio, Mao Shan, Stewart Worrall, Eduardo Nebot
Our approach is capable of using a multi-sensor platform to build a three-dimensional semantic voxelized map that considers the uncertainty of all of the processes involved.
no code implementations • 4 Mar 2020 • Julie Stephany Berrio, Mao Shan, Stewart Worrall, James Ward, Eduardo Nebot
This paper presents an approach to fuse different sensory information, Light Detection and Ranging (lidar) scans and camera images.
2 code implementations • 29 Apr 2019 • Surabhi Verma, Julie Stephany Berrio, Stewart Worrall, Eduardo Nebot
This paper proposes an automated method to obtain the extrinsic calibration parameters between a camera and a 3D lidar with as low as 16 beams.
no code implementations • 24 Oct 2018 • Wei Zhou, Julie Stephany Berrio, Stewart Worrall, Eduardo Nebot
This paper presents a novel method for analysing the robustness of semantic segmentation models and provides a number of metrics to evaluate the classification performance over a variety of environmental conditions.