2,149 research outputs found
Evaluation of CNN-based Single-Image Depth Estimation Methods
While an increasing interest in deep models for single-image depth estimation
methods can be observed, established schemes for their evaluation are still
limited. We propose a set of novel quality criteria, allowing for a more
detailed analysis by focusing on specific characteristics of depth maps. In
particular, we address the preservation of edges and planar regions, depth
consistency, and absolute distance accuracy. In order to employ these metrics
to evaluate and compare state-of-the-art single-image depth estimation
approaches, we provide a new high-quality RGB-D dataset. We used a DSLR camera
together with a laser scanner to acquire high-resolution images and highly
accurate depth maps. Experimental results show the validity of our proposed
evaluation protocol
Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects
We introduce a method based on the deflectometry principle for the
reconstruction of specular objects exhibiting significant size and geometric
complexity. A key feature of our approach is the deployment of an Automatic
Virtual Environment (CAVE) as pattern generator. To unfold the full power of
this extraordinary experimental setup, an optical encoding scheme is developed
which accounts for the distinctive topology of the CAVE. Furthermore, we devise
an algorithm for detecting the object of interest in raw deflectometric images.
The segmented foreground is used for single-view reconstruction, the background
for estimation of the camera pose, necessary for calibrating the sensor system.
Experiments suggest a significant gain of coverage in single measurements
compared to previous methods. To facilitate research on specular surface
reconstruction, we will make our data set publicly available
Semantic Validation in Structure from Motion
The Structure from Motion (SfM) challenge in computer vision is the process
of recovering the 3D structure of a scene from a series of projective
measurements that are calculated from a collection of 2D images, taken from
different perspectives. SfM consists of three main steps; feature detection and
matching, camera motion estimation, and recovery of 3D structure from estimated
intrinsic and extrinsic parameters and features.
A problem encountered in SfM is that scenes lacking texture or with
repetitive features can cause erroneous feature matching between frames.
Semantic segmentation offers a route to validate and correct SfM models by
labelling pixels in the input images with the use of a deep convolutional
neural network. The semantic and geometric properties associated with classes
in the scene can be taken advantage of to apply prior constraints to each class
of object. The SfM pipeline COLMAP and semantic segmentation pipeline DeepLab
were used. This, along with planar reconstruction of the dense model, were used
to determine erroneous points that may be occluded from the calculated camera
position, given the semantic label, and thus prior constraint of the
reconstructed plane. Herein, semantic segmentation is integrated into SfM to
apply priors on the 3D point cloud, given the object detection in the 2D input
images. Additionally, the semantic labels of matched keypoints are compared and
inconsistent semantically labelled points discarded. Furthermore, semantic
labels on input images are used for the removal of objects associated with
motion in the output SfM models. The proposed approach is evaluated on a
data-set of 1102 images of a repetitive architecture scene. This project offers
a novel method for improved validation of 3D SfM models
Reconstruction of hidden 3D shapes using diffuse reflections
We analyze multi-bounce propagation of light in an unknown hidden volume and
demonstrate that the reflected light contains sufficient information to recover
the 3D structure of the hidden scene. We formulate the forward and inverse
theory of secondary and tertiary scattering reflection using ideas from energy
front propagation and tomography. We show that using careful choice of
approximations, such as Fresnel approximation, greatly simplifies this problem
and the inversion can be achieved via a backpropagation process. We provide a
theoretical analysis of the invertibility, uniqueness and choices of
space-time-angle dimensions using synthetic examples. We show that a 2D streak
camera can be used to discover and reconstruct hidden geometry. Using a 1D high
speed time of flight camera, we show that our method can be used recover 3D
shapes of objects "around the corner"
Weighted simplicial complex reconstruction from mobile laser scanning using sensor topology
We propose a new method for the reconstruction of simplicial complexes
(combining points, edges and triangles) from 3D point clouds from Mobile Laser
Scanning (MLS). Our method uses the inherent topology of the MLS sensor to
define a spatial adjacency relationship between points. We then investigate
each possible connexion between adjacent points, weighted according to its
distance to the sensor, and filter them by searching collinear structures in
the scene, or structures perpendicular to the laser beams. Next, we create and
filter triangles for each triplet of self-connected edges and according to
their local planarity. We compare our results to an unweighted simplicial
complex reconstruction.Comment: 8 pages, 11 figures, CFPT 2018. arXiv admin note: substantial text
overlap with arXiv:1802.0748
A geometrical-based approach to recognise structure of complex interiors
3D modelling of building interiors has gained a lot of interest recently, specifically since the
rise of Building Information Modeling (BIM). A number of methods have been developed in
the past, however most of them are limited to modelling non-complex interiors. 3D laser
scanners are the preferred sensor to collect the 3D data, however the cost of state-of-the-art
laser scanners are prohibitive to many. Other types of sensors could also be used to generate
the 3D data but they have limitations especially when dealing with clutter and occlusions.
This research has developed a platform to produce 3D modelling of building interiors while
adapting a low-cost, low-level laser scanner to generate the 3D interior data. The PreSuRe
algorithm developed here, which introduces a new pipeline in modelling building interiors,
combines both novel methods and adapts existing approaches to produce the 3D modelling of
various interiors, from sparse room to complex interiors with non-ideal geometrical structure,
highly cluttered and occluded. This approach has successfully reconstructed the structure of
interiors, with above 96% accuracy, even with high amount of noise data and clutter. The
time taken to produce the resulting model is almost real-time, compared to existing
techniques which may take hours to generate the reconstruction. The produced model is also
equipped with semantic information which differentiates the model from a regular 3D CAD
drawing and can be use to assist professionals and experts in related fields
A FLEXIBLE METHODOLOGY FOR OUTDOOR/INDOOR BUILDING RECONSTRUCTION FROM OCCLUDED POINT CLOUDS
Terrestrial Laser Scanning data are increasingly used in building survey not only in cultural heritage domain but also for as-built modelling of large and medium size civil structures. However, raw point clouds derived from laser scanning generally not directly ready for the generation of such models. A time-consuming manual modelling phase has to be taken into account. In addition the large presence of occlusion and clutter may turn out in low-quality building models when state-of-the-art automatic modelling procedures are applied. This paper presents an automated procedure to convert raw point clouds into semantically-enriched building models. The developed method mainly focuses on a geometrical complexity typical of modern buildings with clear prevalence of planar features A characteristic of this methodology is the possibility to work with outdoor and indoor building environments. In order to operate under severe occlusions and clutter a couple of completion algorithms were designed to generate a plausible and reliable model. Finally, some examples of the developed modelling procedure are presented and discussed
- …