147,756 research outputs found
A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data
In this paper we present a practical approach for generating an
occlusion-free textured 3D map of urban facades by the synergistic use of
terrestrial images, 3D point clouds and area-based information. Particularly in
dense urban environments, the high presence of urban objects in front of the
facades causes significant difficulties for several stages in computational
building modeling. Major challenges lie on the one hand in extracting complete
3D facade quadrilateral delimitations and on the other hand in generating
occlusion-free facade textures. For these reasons, we describe a
straightforward approach for completing and recovering facade geometry and
textures by exploiting the data complementarity of terrestrial multi-source
imagery and area-based information
Statistical Modeling of Craniofacial Shape and Texture
We present a fully-automatic statistical 3D shape modeling approach and apply it to a large dataset of 3D images, the Headspace dataset, thus generating the first public shape-and-texture 3D Morphable Model (3DMM) of the full human head. Our approach is the first to employ a template that adapts to the dataset subject before dense morphing. This is fully automatic and achieved using 2D facial landmarking, projection to 3D shape, and mesh editing. In dense template morphing, we improve on the well-known Coherent Point Drift algorithm, by incorporating iterative data-sampling and alignment. Our evaluations demonstrate that our method has better performance in correspondence accuracy and modeling ability when compared with other competing algorithms. We propose a texture map refinement scheme to build high quality texture maps and texture model. We present several applications that include the first clinical use of craniofacial 3DMMs in the assessment of different types of surgical intervention applied to a craniosynostosis patient group
Contextual Modeling for 3D Dense Captioning on Point Clouds
3D dense captioning, as an emerging vision-language task, aims to identify
and locate each object from a set of point clouds and generate a distinctive
natural language sentence for describing each located object. However, the
existing methods mainly focus on mining inter-object relationship, while
ignoring contextual information, especially the non-object details and
background environment within the point clouds, thus leading to low-quality
descriptions, such as inaccurate relative position information. In this paper,
we make the first attempt to utilize the point clouds clustering features as
the contextual information to supply the non-object details and background
environment of the point clouds and incorporate them into the 3D dense
captioning task. We propose two separate modules, namely the Global Context
Modeling (GCM) and Local Context Modeling (LCM), in a coarse-to-fine manner to
perform the contextual modeling of the point clouds. Specifically, the GCM
module captures the inter-object relationship among all objects with global
contextual information to obtain more complete scene information of the whole
point clouds. The LCM module exploits the influence of the neighboring objects
of the target object and local contextual information to enrich the object
representations. With such global and local contextual modeling strategies, our
proposed model can effectively characterize the object representations and
contextual information and thereby generate comprehensive and detailed
descriptions of the located objects. Extensive experiments on the ScanRefer and
Nr3D datasets demonstrate that our proposed method sets a new record on the 3D
dense captioning task, and verify the effectiveness of our raised contextual
modeling of point clouds
Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings
Dense prediction tasks are common for 3D point clouds, but the uncertainties inherent in massive points and their embeddings have long been ignored. In this work, we present CUE, a novel uncertainty estimation method for dense prediction tasks in 3D point clouds. Inspired by metric learning, the key idea of CUE is to explore cross-point embeddings upon a conventional 3D dense prediction pipeline. Specifically, CUE involves building a probabilistic embedding model and then enforcing metric alignments of massive points in the embedding space. We also propose CUE+, which enhances CUE by explicitly modeling cross-point dependencies in the covariance matrix. We demonstrate that both CUE and CUE+ are generic and effective for uncertainty estimation in 3D point clouds with two different tasks: (1) in 3D geometric feature learning we for the first time obtain well-calibrated uncertainty, and (2) in semantic segmentation we reduce uncertainty's Expected Calibration Error of the state-of-the-arts by 16.5%. All uncertainties are estimated without compromising predictive performance
Protected subspace Ramsey spectroscopy
We study a modified Ramsey spectroscopy technique employing slowly decaying
states for quantum metrology applications using dense ensembles. While closely
positioned atoms exhibit superradiant collective decay and dipole-dipole
induced frequency shifts, recent results [Ostermann, Ritsch and Genes, Phys.
Rev. Lett. \textbf{111}, 123601 (2013)] suggest the possibility to suppress
such detrimental effects and achieve an even better scaling of the frequency
sensitivity with interrogation time than for noninteracting particles. Here we
present an in-depth analysis of this 'protected subspace Ramsey technique'
using improved analytical modeling and numerical simulations including larger
3D samples. Surprisingly we find that using sub-radiant states of particles
to encode the atomic coherence yields a scaling of the optimal sensitivity
better than . Applied to ultracold atoms in 3D optical lattices we
predict a precision beyond the single atom linewidth.Comment: 9 pages, 7 figure
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