117,005 research outputs found
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
Point clouds obtained with 3D scanners or by image-based reconstruction
techniques are often corrupted with significant amount of noise and outliers.
Traditional methods for point cloud denoising largely rely on local surface
fitting (e.g., jets or MLS surfaces), local or non-local averaging, or on
statistical assumptions about the underlying noise model. In contrast, we
develop a simple data-driven method for removing outliers and reducing noise in
unordered point clouds. We base our approach on a deep learning architecture
adapted from PCPNet, which was recently proposed for estimating local 3D shape
properties in point clouds. Our method first classifies and discards outlier
samples, and then estimates correction vectors that project noisy points onto
the original clean surfaces. The approach is efficient and robust to varying
amounts of noise and outliers, while being able to handle large densely-sampled
point clouds. In our extensive evaluation, both on synthesic and real data, we
show an increased robustness to strong noise levels compared to various
state-of-the-art methods, enabling accurate surface reconstruction from
extremely noisy real data obtained by range scans. Finally, the simplicity and
universality of our approach makes it very easy to integrate in any existing
geometry processing pipeline
An Octree-Based Approach towards Efficient Variational Range Data Fusion
Volume-based reconstruction is usually expensive both in terms of memory
consumption and runtime. Especially for sparse geometric structures, volumetric
representations produce a huge computational overhead. We present an efficient
way to fuse range data via a variational Octree-based minimization approach by
taking the actual range data geometry into account. We transform the data into
Octree-based truncated signed distance fields and show how the optimization can
be conducted on the newly created structures. The main challenge is to uphold
speed and a low memory footprint without sacrificing the solutions' accuracy
during optimization. We explain how to dynamically adjust the optimizer's
geometric structure via joining/splitting of Octree nodes and how to define the
operators. We evaluate on various datasets and outline the suitability in terms
of performance and geometric accuracy.Comment: BMVC 201
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and
Automation (ICRA '15), Seattle, WA, US
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