1,596 research outputs found
A Deep Primal-Dual Network for Guided Depth Super-Resolution
In this paper we present a novel method to increase the spatial resolution of
depth images. We combine a deep fully convolutional network with a non-local
variational method in a deep primal-dual network. The joint network computes a
noise-free, high-resolution estimate from a noisy, low-resolution input depth
map. Additionally, a high-resolution intensity image is used to guide the
reconstruction in the network. By unrolling the optimization steps of a
first-order primal-dual algorithm and formulating it as a network, we can train
our joint method end-to-end. This not only enables us to learn the weights of
the fully convolutional network, but also to optimize all parameters of the
variational method and its optimization procedure. The training of such a deep
network requires a large dataset for supervision. Therefore, we generate
high-quality depth maps and corresponding color images with a physically based
renderer. In an exhaustive evaluation we show that our method outperforms the
state-of-the-art on multiple benchmarks.Comment: BMVC 201
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
A monitoring and diagnostic approach for stochastic textured surfaces
We develop a supervised-learning-based approach for monitoring and diagnosing
texture-related defects in manufactured products characterized by stochastic
textured surfaces that satisfy the locality and stationarity properties of
Markov random fields. Examples of stochastic textured surface data include
images of woven textiles; image or surface metrology data for machined, cast,
or formed metal parts; microscopy images of material microstructure samples;
etc. To characterize the complex spatial statistical dependencies of in-control
samples of the stochastic textured surface, we use rather generic supervised
learning methods, which provide an implicit characterization of the joint
distribution of the surface texture. We propose two spatial moving statistics,
which are computed from residual errors of the fitted supervised learning
model, for monitoring and diagnosing local aberrations in the general spatial
statistical behavior of newly manufactured stochastic textured surface samples
in a statistical process control context. We illustrate the approach using
images of textile fabric samples and simulated 2-D stochastic processes, for
which the algorithm successfully detects local defects of various natures.
Supplemental discussions, results, data and computer codes are available
online
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
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution
High-resolution depth maps can be inferred from low-resolution depth
measurements and an additional high-resolution intensity image of the same
scene. To that end, we introduce a bimodal co-sparse analysis model, which is
able to capture the interdependency of registered intensity and depth
information. This model is based on the assumption that the co-supports of
corresponding bimodal image structures are aligned when computed by a suitable
pair of analysis operators. No analytic form of such operators exist and we
propose a method for learning them from a set of registered training signals.
This learning process is done offline and returns a bimodal analysis operator
that is universally applicable to natural scenes. We use this to exploit the
bimodal co-sparse analysis model as a prior for solving inverse problems, which
leads to an efficient algorithm for depth map super-resolution.Comment: 13 pages, 4 figure
Stereo and ToF Data Fusion by Learning from Synthetic Data
Time-of-Flight (ToF) sensors and stereo vision systems are both capable of acquiring depth information but they have complementary characteristics and issues. A more accurate representation of the scene geometry can be obtained by fusing the two depth sources. In this paper we present a novel framework for data fusion where the contribution of the two depth sources is controlled by confidence measures that are jointly estimated using a Convolutional Neural Network. The two depth sources are fused enforcing the local consistency of depth data, taking into account the estimated confidence information. The deep network is trained using a synthetic dataset and we show how the classifier is able to generalize to different data, obtaining reliable estimations not only on synthetic data but also on real world scenes. Experimental results show that the proposed approach increases the accuracy of the depth estimation on both synthetic and real data and that it is able to outperform state-of-the-art methods
PetroSurf3D - A Dataset for high-resolution 3D Surface Segmentation
The development of powerful 3D scanning hardware and reconstruction
algorithms has strongly promoted the generation of 3D surface reconstructions
in different domains. An area of special interest for such 3D reconstructions
is the cultural heritage domain, where surface reconstructions are generated to
digitally preserve historical artifacts. While reconstruction quality nowadays
is sufficient in many cases, the robust analysis (e.g. segmentation, matching,
and classification) of reconstructed 3D data is still an open topic. In this
paper, we target the automatic and interactive segmentation of high-resolution
3D surface reconstructions from the archaeological domain. To foster research
in this field, we introduce a fully annotated and publicly available
large-scale 3D surface dataset including high-resolution meshes, depth maps and
point clouds as a novel benchmark dataset to the community. We provide baseline
results for our existing random forest-based approach and for the first time
investigate segmentation with convolutional neural networks (CNNs) on the data.
Results show that both approaches have complementary strengths and weaknesses
and that the provided dataset represents a challenge for future research.Comment: CBMI Submission; Dataset and more information can be found at
http://lrs.icg.tugraz.at/research/petroglyphsegmentation
- …