Semantic structure from motion with points, regions, and objects
By Sid Ying-Ze Bao, Mohit Bagra, Yu-Wei Chao, Silvio Savarese. 2012
116 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Andrew J. Davison
- Renato F. Salas-Moreno
- Ben Glocker
- Shuran Song
- Hauke Strasdat
- Stefan Leutenegger
- Shichao Yang
- John J. Leonard
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Kristen Grauman
- Jitendra Malik
- Andrew J. Davison
- Dacheng Tao
- Vladlen Koltun
- Trevor Darrell
- Andrew Zisserman
In Geology
- Andrew J. Davison
- Shuran Song
- Ben Glocker
- Renato F. Salas-Moreno
- Sebastian A. Scherer
- Kristen Grauman
- John J. Leonard
- Richard A. Newcombe
Citations in the corpus
- Continuous Scene Representations for Embodied AI (2022, background): “There are object-based maps that encode object-level semantics and the relationships [3, 7, 42].”
- Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “muddy-road while driving), move from path-planning to taskplanning, and enable advanced human-robot interaction [10],”
- Occupancy Anticipation for Efficient Exploration and Navigation (2020, background): “Navigation Classical approaches to visual navigation perform passive or active SLAM to reconstruct geometric point-clouds [71,23] or semantic maps [5,55], facilitated by loop closures or learned odometry [7,39,8].”
- Monocular SLAM Supported Object Recognition (2015, background): “Bao et al. [2, 3] proposed one of the first approaches to jointly estimate camera parameters, scene points and object labels using both geometric and semantic attributes in the scene.”
- DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments (2018, background): “[14] first try to estimate camera pose, points, and objects jointly utilizing both geometry and semantic attributes in the scene, which significantly improve the object recognition accuracy.”
- CubeSLAM: Monocular 3-D Object SLAM (2018, background): “Bao et al. proposed the first Semantic SfM to jointly optimize camera poses, objects, points and planes [19].”
- PL-SLAM: Real-time monocular visual SLAM with points and lines (2017, methodology): “Scene prior information have been also exploited to provide a significant boost to SLAM systems [3], [4].”
- Dense planar SLAM (2014, background): “[3], is far from being feasible in real-time operation, unlike [21].”
- SLAM++: Simultaneous Localisation and Mapping at the Level of Objects (2013, background): “[3] represented a scene as a set of points, objects and regions in two-view SfM, solving ex-pensively and jointly in a graph optimisation for a labelling and reconstruction solution taking account of interactions between all scene entities.”