15,261 research outputs found
Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction
State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors
usually reduce drift in camera tracking by globally optimizing the estimated
camera poses in real-time without simultaneously updating the reconstructed
surface on pose changes. We propose an efficient on-the-fly surface correction
method for globally consistent dense 3D reconstruction of large-scale scenes.
Our approach uses a dense Visual RGB-D SLAM system that estimates the camera
motion in real-time on a CPU and refines it in a global pose graph
optimization. Consecutive RGB-D frames are locally fused into keyframes, which
are incorporated into a sparse voxel hashed Signed Distance Field (SDF) on the
GPU. On pose graph updates, the SDF volume is corrected on-the-fly using a
novel keyframe re-integration strategy with reduced GPU-host streaming. We
demonstrate in an extensive quantitative evaluation that our method is up to
93% more runtime efficient compared to the state-of-the-art and requires
significantly less memory, with only negligible loss of surface quality.
Overall, our system requires only a single GPU and allows for real-time surface
correction of large environments.Comment: British Machine Vision Conference (BMVC), London, September 201
Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction
State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors
usually reduce drift in camera tracking by globally optimizing the estimated
camera poses in real-time without simultaneously updating the reconstructed
surface on pose changes. We propose an efficient on-the-fly surface correction
method for globally consistent dense 3D reconstruction of large-scale scenes.
Our approach uses a dense Visual RGB-D SLAM system that estimates the camera
motion in real-time on a CPU and refines it in a global pose graph
optimization. Consecutive RGB-D frames are locally fused into keyframes, which
are incorporated into a sparse voxel hashed Signed Distance Field (SDF) on the
GPU. On pose graph updates, the SDF volume is corrected on-the-fly using a
novel keyframe re-integration strategy with reduced GPU-host streaming. We
demonstrate in an extensive quantitative evaluation that our method is up to
93% more runtime efficient compared to the state-of-the-art and requires
significantly less memory, with only negligible loss of surface quality.
Overall, our system requires only a single GPU and allows for real-time surface
correction of large environments.Comment: British Machine Vision Conference (BMVC), London, September 201
A machine learning route between band mapping and band structure
The electronic band structure (BS) of solid state materials imprints the
multidimensional and multi-valued functional relations between energy and
momenta of periodically confined electrons. Photoemission spectroscopy is a
powerful tool for its comprehensive characterization. A common task in
photoemission band mapping is to recover the underlying quasiparticle
dispersion, which we call band structure reconstruction. Traditional methods
often focus on specific regions of interests yet require extensive human
oversight. To cope with the growing size and scale of photoemission data, we
develop a generic machine-learning approach leveraging the information within
electronic structure calculations for this task. We demonstrate its capability
by reconstructing all fourteen valence bands of tungsten diselenide and
validate the accuracy on various synthetic data. The reconstruction uncovers
previously inaccessible momentum-space structural information on both global
and local scales in conjunction with theory, while realizing a path towards
integrating band mapping data into materials science databases
GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion
Many monocular visual SLAM algorithms are derived from incremental
structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM
method which integrates recent advances made in global SfM. In particular, we
present two main contributions to visual SLAM. First, we solve the visual
odometry problem by a novel rank-1 matrix factorization technique which is more
robust to the errors in map initialization. Second, we adopt a recent global
SfM method for the pose-graph optimization, which leads to a multi-stage linear
formulation and enables L1 optimization for better robustness to false loops.
The combination of these two approaches generates more robust reconstruction
and is significantly faster (4X) than recent state-of-the-art SLAM systems. We
also present a new dataset recorded with ground truth camera motion in a Vicon
motion capture room, and compare our method to prior systems on it and
established benchmark datasets.Comment: 3DV 2017 Project Page: https://frobelbest.github.io/gsla
- …