522 research outputs found
Antisolvent addition at extreme conditions
This article describes the use of antisolvent addition at high-pressure to aid precipitation and recovery of high-pressure phases to ambient pressure. Paracetamol (PCM) was used as a model system to demonstrate the principle due to the extensive literature of paracetamol at high-pressure and ambient pressure. We have observed that we are able to recover the orthorhombic form of paracetamol to ambient pressure using this technique, although solvent-mediated transformations are a hurdle. During this investigation we observed a new methanol solvate of paracetamol that is simlar in structure to the known form. The methanol solvate is stable to 0.2 GPa before transformation to the orthorhombic form that is known to be the stable form at high pressure
The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes
Estimating camera motion in deformable scenes poses a complex and open
research challenge. Most existing non-rigid structure from motion techniques
assume to observe also static scene parts besides deforming scene parts in
order to establish an anchoring reference. However, this assumption does not
hold true in certain relevant application cases such as endoscopies. Deformable
odometry and SLAM pipelines, which tackle the most challenging scenario of
exploratory trajectories, suffer from a lack of robustness and proper
quantitative evaluation methodologies. To tackle this issue with a common
benchmark, we introduce the Drunkard's Dataset, a challenging collection of
synthetic data targeting visual navigation and reconstruction in deformable
environments. This dataset is the first large set of exploratory camera
trajectories with ground truth inside 3D scenes where every surface exhibits
non-rigid deformations over time. Simulations in realistic 3D buildings lets us
obtain a vast amount of data and ground truth labels, including camera poses,
RGB images and depth, optical flow and normal maps at high resolution and
quality. We further present a novel deformable odometry method, dubbed the
Drunkard's Odometry, which decomposes optical flow estimates into rigid-body
camera motion and non-rigid scene deformations. In order to validate our data,
our work contains an evaluation of several baselines as well as a novel
tracking error metric which does not require ground truth data. Dataset and
code: https://davidrecasens.github.io/TheDrunkard'sOdometry
Semantically Informed Multiview Surface Refinement
We present a method to jointly refine the geometry and semantic segmentation
of 3D surface meshes. Our method alternates between updating the shape and the
semantic labels. In the geometry refinement step, the mesh is deformed with
variational energy minimization, such that it simultaneously maximizes
photo-consistency and the compatibility of the semantic segmentations across a
set of calibrated images. Label-specific shape priors account for interactions
between the geometry and the semantic labels in 3D. In the semantic
segmentation step, the labels on the mesh are updated with MRF inference, such
that they are compatible with the semantic segmentations in the input images.
Also, this step includes prior assumptions about the surface shape of different
semantic classes. The priors induce a tight coupling, where semantic
information influences the shape update and vice versa. Specifically, we
introduce priors that favor (i) adaptive smoothing, depending on the class
label; (ii) straightness of class boundaries; and (iii) semantic labels that
are consistent with the surface orientation. The novel mesh-based
reconstruction is evaluated in a series of experiments with real and synthetic
data. We compare both to state-of-the-art, voxel-based semantic 3D
reconstruction, and to purely geometric mesh refinement, and demonstrate that
the proposed scheme yields improved 3D geometry as well as an improved semantic
segmentation
Learned Semantic Multi-Sensor Depth Map Fusion
Volumetric depth map fusion based on truncated signed distance functions has
become a standard method and is used in many 3D reconstruction pipelines. In
this paper, we are generalizing this classic method in multiple ways: 1)
Semantics: Semantic information enriches the scene representation and is
incorporated into the fusion process. 2) Multi-Sensor: Depth information can
originate from different sensors or algorithms with very different noise and
outlier statistics which are considered during data fusion. 3) Scene denoising
and completion: Sensors can fail to recover depth for certain materials and
light conditions, or data is missing due to occlusions. Our method denoises the
geometry, closes holes and computes a watertight surface for every semantic
class. 4) Learning: We propose a neural network reconstruction method that
unifies all these properties within a single powerful framework. Our method
learns sensor or algorithm properties jointly with semantic depth fusion and
scene completion and can also be used as an expert system, e.g. to unify the
strengths of various photometric stereo algorithms. Our approach is the first
to unify all these properties. Experimental evaluations on both synthetic and
real data sets demonstrate clear improvements.Comment: 11 pages, 7 figures, 2 tables, accepted for the 2nd Workshop on 3D
Reconstruction in the Wild (3DRW2019) in conjunction with ICCV201
Point-SLAM: Dense Neural Point Cloud-based SLAM
We propose a dense neural simultaneous localization and mapping (SLAM)
approach for monocular RGBD input which anchors the features of a neural scene
representation in a point cloud that is iteratively generated in an
input-dependent data-driven manner. We demonstrate that both tracking and
mapping can be performed with the same point-based neural scene representation
by minimizing an RGBD-based re-rendering loss. In contrast to recent dense
neural SLAM methods which anchor the scene features in a sparse grid, our
point-based approach allows dynamically adapting the anchor point density to
the information density of the input. This strategy reduces runtime and memory
usage in regions with fewer details and dedicates higher point density to
resolve fine details. Our approach performs either better or competitive to
existing dense neural RGBD SLAM methods in tracking, mapping and rendering
accuracy on the Replica, TUM-RGBD and ScanNet datasets. The source code is
available at https://github.com/tfy14esa/Point-SLAM.Comment: 17 Pages, 10 Figure
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