63 citations · 87 across the 5 of their papers we have counts for
7 papers
SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation
Jingwen Wang, Juan Tarrio, Lourdes Agapito +2
The availability of real-time semantics greatly improves the core geometric functionality of SLAM systems, enabling numerous robotic and AR/VR applications. We present a new method…
Self-Supervised Depth Completion for Active Stereo
Frederik Warburg, Daniel Hernandez-Juarez, Juan Tarrio +3
Active stereo systems are used in many robotic applications that require 3D information. These depth sensors, however, suffer from stereo artefacts and do not provide dense depth e…
Multi-Resolution 3D Mapping with Explicit Free Space Representation for Fast and Accurate Mobile Robot Motion Planning
Nils Funk, Juan Tarrio, Sotiris Papatheodorou +3
With the aim of bridging the gap between high quality reconstruction and mobile robot motion planning, we propose an efficient system that leverages the concept of adaptive-resolut…
Towards Bounding-Box Free Panoptic Segmentation
Ujwal Bonde, Pablo F. Alcantarilla, Stefan Leutenegger
In this work we introduce a new Bounding-Box Free Network (BBFNet) for panoptic segmentation. Panoptic segmentation is an ideal problem for proposal-free methods as it already requ…
A Continuous Optimization Approach for Efficient and Accurate Scene Flow
Zhaoyang Lv, Chris Beall, Pablo F. Alcantarilla +3
We propose a continuous optimization method for solving dense 3D scene flow problems from stereo imagery. As in recent work, we represent the dynamic 3D scene as a collection of ri…
Noise Models in Feature-based Stereo Visual Odometry
Pablo F. Alcantarilla, Oliver J. Woodford
Feature-based visual structure and motion reconstruction pipelines, common in visual odometry and large-scale reconstruction from photos, use the location of corresponding features…
Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation
German Ros, Simon Stent, Pablo F. Alcantarilla +1
In this work we investigate the problem of road scene semantic segmentation using Deconvolutional Networks (DNs). Several constraints limit the practical performance of DNs in this…