13,871 research outputs found
Semi-Global Stereo Matching with Surface Orientation Priors
Semi-Global Matching (SGM) is a widely-used efficient stereo matching
technique. It works well for textured scenes, but fails on untextured slanted
surfaces due to its fronto-parallel smoothness assumption. To remedy this
problem, we propose a simple extension, termed SGM-P, to utilize precomputed
surface orientation priors. Such priors favor different surface slants in
different 2D image regions or 3D scene regions and can be derived in various
ways. In this paper we evaluate plane orientation priors derived from stereo
matching at a coarser resolution and show that such priors can yield
significant performance gains for difficult weakly-textured scenes. We also
explore surface normal priors derived from Manhattan-world assumptions, and we
analyze the potential performance gains using oracle priors derived from
ground-truth data. SGM-P only adds a minor computational overhead to SGM and is
an attractive alternative to more complex methods employing higher-order
smoothness terms.Comment: extended draft of 3DV 2017 (spotlight) pape
Plane extraction for indoor place recognition
In this paper, we present an image based plane extraction
method well suited for real-time operations. Our approach exploits the
assumption that the surrounding scene is mainly composed by planes
disposed in known directions. Planes are detected from a single image
exploiting a voting scheme that takes into account the vanishing lines.
Then, candidate planes are validated and merged using a region grow-
ing based approach to detect in real-time planes inside an unknown in-
door environment. Using the related plane homographies is possible to
remove the perspective distortion, enabling standard place recognition
algorithms to work in an invariant point of view setup. Quantitative Ex-
periments performed with real world images show the effectiveness of our
approach compared with a very popular method
Dense Piecewise Planar RGB-D SLAM for Indoor Environments
The paper exploits weak Manhattan constraints to parse the structure of
indoor environments from RGB-D video sequences in an online setting. We extend
the previous approach for single view parsing of indoor scenes to video
sequences and formulate the problem of recovering the floor plan of the
environment as an optimal labeling problem solved using dynamic programming.
The temporal continuity is enforced in a recursive setting, where labeling from
previous frames is used as a prior term in the objective function. In addition
to recovery of piecewise planar weak Manhattan structure of the extended
environment, the orthogonality constraints are also exploited by visual
odometry and pose graph optimization. This yields reliable estimates in the
presence of large motions and absence of distinctive features to track. We
evaluate our method on several challenging indoors sequences demonstrating
accurate SLAM and dense mapping of low texture environments. On existing TUM
benchmark we achieve competitive results with the alternative approaches which
fail in our environments.Comment: International Conference on Intelligent Robots and Systems (IROS)
201
Layered Interpretation of Street View Images
We propose a layered street view model to encode both depth and semantic
information on street view images for autonomous driving. Recently, stixels,
stix-mantics, and tiered scene labeling methods have been proposed to model
street view images. We propose a 4-layer street view model, a compact
representation over the recently proposed stix-mantics model. Our layers encode
semantic classes like ground, pedestrians, vehicles, buildings, and sky in
addition to the depths. The only input to our algorithm is a pair of stereo
images. We use a deep neural network to extract the appearance features for
semantic classes. We use a simple and an efficient inference algorithm to
jointly estimate both semantic classes and layered depth values. Our method
outperforms other competing approaches in Daimler urban scene segmentation
dataset. Our algorithm is massively parallelizable, allowing a GPU
implementation with a processing speed about 9 fps.Comment: The paper will be presented in the 2015 Robotics: Science and Systems
Conference (RSS
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