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Javier Civera

42 papers hereh-index 408.2k citations118 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author16
  • last author25

Across the 42 of 42 papers where every author was matched, so the position is known.

fields
  • cs.CV29
  • cs.RO11
  • eess.IV1
  • stat.ME1
same name
  • Javier Civera — 16 papers, h 5
  • Javier Civera — 8 papers, h 2
  • Javier Civera — 7 papers, h 3
  • Javier Civera — 5 papers, h 4
  • Javier Civera — 5 papers, h 2
  • Javier Civera — 2 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162023
most citedDynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

1.3k citations · 1.6k across the 35 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.RO2018

The Rosario Dataset: Multisensor Data for Localization and Mapping in Agricultural Environments

Taihú Pire, Martín Mujica, Javier Civera +1

In this paper we present The Rosario Dataset, a collection of sensor data for autonomous mobile robotics in agricultural scenes. The dataset is motivated by the lack of realistic s…

cs.CV2018★ 64 cited

Single-View Place Recognition under Seasonal Changes

Daniel Olid, José M. Fácil, Javier Civera

Single-view place recognition, that we can define as finding an image that corresponds to the same place as a given query image, is a key capability for autonomous navigation and m…

cs.CV2018★ 63 cited

Loosely-Coupled Semi-Direct Monocular SLAM

Seong Hun Lee, Javier Civera

We propose a novel semi-direct approach for monocular simultaneous localization and mapping (SLAM) that combines the complementary strengths of direct and feature-based methods. Th…

cs.CV2018★ 1.3k cited

DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

Berta Bescos, José M. Fácil, Javier Civera +1

The assumption of scene rigidity is typical in SLAM algorithms. Such a strong assumption limits the use of most visual SLAM systems in populated real-world environments, which are…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.