13,618 research outputs found
Capturing natural-colour 3D models of insects for species discovery
Collections of biological specimens are fundamental to scientific
understanding and characterization of natural diversity. This paper presents a
system for liberating useful information from physical collections by bringing
specimens into the digital domain so they can be more readily shared, analyzed,
annotated and compared. It focuses on insects and is strongly motivated by the
desire to accelerate and augment current practices in insect taxonomy which
predominantly use text, 2D diagrams and images to describe and characterize
species. While these traditional kinds of descriptions are informative and
useful, they cannot cover insect specimens "from all angles" and precious
specimens are still exchanged between researchers and collections for this
reason. Furthermore, insects can be complex in structure and pose many
challenges to computer vision systems. We present a new prototype for a
practical, cost-effective system of off-the-shelf components to acquire
natural-colour 3D models of insects from around 3mm to 30mm in length. Colour
images are captured from different angles and focal depths using a digital
single lens reflex (DSLR) camera rig and two-axis turntable. These 2D images
are processed into 3D reconstructions using software based on a visual hull
algorithm. The resulting models are compact (around 10 megabytes), afford
excellent optical resolution, and can be readily embedded into documents and
web pages, as well as viewed on mobile devices. The system is portable, safe,
relatively affordable, and complements the sort of volumetric data that can be
acquired by computed tomography. This system provides a new way to augment the
description and documentation of insect species holotypes, reducing the need to
handle or ship specimens. It opens up new opportunities to collect data for
research, education, art, entertainment, biodiversity assessment and
biosecurity control.Comment: 24 pages, 17 figures, PLOS ONE journa
Mesh-based 3D Textured Urban Mapping
In the era of autonomous driving, urban mapping represents a core step to let
vehicles interact with the urban context. Successful mapping algorithms have
been proposed in the last decade building the map leveraging on data from a
single sensor. The focus of the system presented in this paper is twofold: the
joint estimation of a 3D map from lidar data and images, based on a 3D mesh,
and its texturing. Indeed, even if most surveying vehicles for mapping are
endowed by cameras and lidar, existing mapping algorithms usually rely on
either images or lidar data; moreover both image-based and lidar-based systems
often represent the map as a point cloud, while a continuous textured mesh
representation would be useful for visualization and navigation purposes. In
the proposed framework, we join the accuracy of the 3D lidar data, and the
dense information and appearance carried by the images, in estimating a
visibility consistent map upon the lidar measurements, and refining it
photometrically through the acquired images. We evaluate the proposed framework
against the KITTI dataset and we show the performance improvement with respect
to two state of the art urban mapping algorithms, and two widely used surface
reconstruction algorithms in Computer Graphics.Comment: accepted at iros 201
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Learning based methods have shown very promising results for the task of
depth estimation in single images. However, most existing approaches treat
depth prediction as a supervised regression problem and as a result, require
vast quantities of corresponding ground truth depth data for training. Just
recording quality depth data in a range of environments is a challenging
problem. In this paper, we innovate beyond existing approaches, replacing the
use of explicit depth data during training with easier-to-obtain binocular
stereo footage.
We propose a novel training objective that enables our convolutional neural
network to learn to perform single image depth estimation, despite the absence
of ground truth depth data. Exploiting epipolar geometry constraints, we
generate disparity images by training our network with an image reconstruction
loss. We show that solving for image reconstruction alone results in poor
quality depth images. To overcome this problem, we propose a novel training
loss that enforces consistency between the disparities produced relative to
both the left and right images, leading to improved performance and robustness
compared to existing approaches. Our method produces state of the art results
for monocular depth estimation on the KITTI driving dataset, even outperforming
supervised methods that have been trained with ground truth depth.Comment: CVPR 2017 ora
High-resolution ab initio three-dimensional X-ray diffraction microscopy
Coherent X-ray diffraction microscopy is a method of imaging non-periodic
isolated objects at resolutions only limited, in principle, by the largest
scattering angles recorded. We demonstrate X-ray diffraction imaging with high
resolution in all three dimensions, as determined by a quantitative analysis of
the reconstructed volume images. These images are retrieved from the 3D
diffraction data using no a priori knowledge about the shape or composition of
the object, which has never before been demonstrated on a non-periodic object.
We also construct 2D images of thick objects with infinite depth of focus
(without loss of transverse spatial resolution). These methods can be used to
image biological and materials science samples at high resolution using X-ray
undulator radiation, and establishes the techniques to be used in
atomic-resolution ultrafast imaging at X-ray free-electron laser sources.Comment: 22 pages, 11 figures, submitte
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