Search Results for author: Victor Vaquero

Found 6 papers, 0 papers with code

FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving

no code implementations11 Oct 2019 Hazem Rashed, Mohamed Ramzy, Victor Vaquero, Ahmad El Sallab, Ganesh Sistu, Senthil Yogamani

In this work, we propose a robust and real-time CNN architecture for Moving Object Detection (MOD) under low-light conditions by capturing motion information from both camera and LiDAR sensors.

Autonomous Driving Moving Object Detection +2

Improving Map Re-localization with Deep 'Movable' Objects Segmentation on 3D LiDAR Point Clouds

no code implementations8 Oct 2019 Victor Vaquero, Kai Fischer, Francesc Moreno-Noguer, Alberto Sanfeliu, Stefan Milz

Results show that we are able to accurately re-locate over a filtered map, consistently reducing trajectory errors between an average of 35. 1% with respect to a non-filtered map version and of 47. 9% with respect to a standalone map created on the current session.

Autonomous Vehicles

Hallucinating Dense Optical Flow from Sparse Lidar for Autonomous Vehicles

no code implementations30 Aug 2018 Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer

In this paper we propose a novel approach to estimate dense optical flow from sparse lidar data acquired on an autonomous vehicle.

Autonomous Vehicles Optical Flow Estimation

Deep Lidar CNN to Understand the Dynamics of Moving Vehicles

no code implementations28 Aug 2018 Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer

Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning.

Autonomous Driving Optical Flow Estimation

Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios

no code implementations23 Aug 2018 Victor Vaquero, Ivan del Pino, Francesc Moreno-Noguer, Joan Solà, Alberto Sanfeliu, Juan Andrade-Cetto

The system is thoroughly evaluated on the KITTI tracking dataset, and we show the performance boost provided by our CNN-based vehicle detector over a standard geometric approach.

Autonomous Driving Multi-Object Tracking

Joint Coarse-And-Fine Reasoning for Deep Optical Flow

no code implementations22 Aug 2018 Victor Vaquero, German Ros, Francesc Moreno-Noguer, Antonio M. Lopez, Alberto Sanfeliu

We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning.

Optical Flow Estimation

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