347 research outputs found
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
The monocular vision-based simultaneous localization and mapping (vSLAM) is
one of the most challenging problem in mobile robotics and computer vision. In
this work we study the post-processing techniques applied to sparse 3D
point-cloud maps, obtained by feature-based vSLAM algorithms. Map
post-processing is split into 2 major steps: 1) noise and outlier removal and
2) upsampling. We evaluate different combinations of known algorithms for
outlier removing and upsampling on datasets of real indoor and outdoor
environments and identify the most promising combination. We further use it to
convert a point-cloud map, obtained by the real UAV performing indoor flight to
3D voxel grid (octo-map) potentially suitable for path planning.Comment: 10 pages, 4 figures, camera-ready version of paper for "The 3rd
International Conference on Interactive Collaborative Robotics (ICR 2018)
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
This paper addresses the problem of generating uniform dense point clouds to
describe the underlying geometric structures from given sparse point clouds.
Due to the irregular and unordered nature, point cloud densification as a
generative task is challenging. To tackle the challenge, we propose a novel
deep neural network based method, called PUGeo-Net, that learns a
linear transformation matrix for each input point. Matrix
approximates the augmented Jacobian matrix of a local parameterization and
builds a one-to-one correspondence between the 2D parametric domain and the 3D
tangent plane so that we can lift the adaptively distributed 2D samples (which
are also learned from data) to 3D space. After that, we project the samples to
the curved surface by computing a displacement along the normal of the tangent
plane. PUGeo-Net is fundamentally different from the existing deep learning
methods that are largely motivated by the image super-resolution techniques and
generate new points in the abstract feature space. Thanks to its
geometry-centric nature, PUGeo-Net works well for both CAD models with sharp
features and scanned models with rich geometric details. Moreover, PUGeo-Net
can compute the normal for the original and generated points, which is highly
desired by the surface reconstruction algorithms. Computational results show
that PUGeo-Net, the first neural network that can jointly generate vertex
coordinates and normals, consistently outperforms the state-of-the-art in terms
of accuracy and efficiency for upsampling factor .Comment: 17 pages, 10 figure
iPUNet:Iterative Cross Field Guided Point Cloud Upsampling
Point clouds acquired by 3D scanning devices are often sparse, noisy, and
non-uniform, causing a loss of geometric features. To facilitate the usability
of point clouds in downstream applications, given such input, we present a
learning-based point upsampling method, i.e., iPUNet, which generates dense and
uniform points at arbitrary ratios and better captures sharp features. To
generate feature-aware points, we introduce cross fields that are aligned to
sharp geometric features by self-supervision to guide point generation. Given
cross field defined frames, we enable arbitrary ratio upsampling by learning at
each input point a local parameterized surface. The learned surface consumes
the neighboring points and 2D tangent plane coordinates as input, and maps onto
a continuous surface in 3D where arbitrary ratios of output points can be
sampled. To solve the non-uniformity of input points, on top of the cross field
guided upsampling, we further introduce an iterative strategy that refines the
point distribution by moving sparse points onto the desired continuous 3D
surface in each iteration. Within only a few iterations, the sparse points are
evenly distributed and their corresponding dense samples are more uniform and
better capture geometric features. Through extensive evaluations on diverse
scans of objects and scenes, we demonstrate that iPUNet is robust to handle
noisy and non-uniformly distributed inputs, and outperforms state-of-the-art
point cloud upsampling methods
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
To better address challenging issues of the irregularity and inhomogeneity
inherently present in 3D point clouds, researchers have been shifting their
focus from the design of hand-craft point feature towards the learning of 3D
point signatures using deep neural networks for 3D point cloud classification.
Recent proposed deep learning based point cloud classification methods either
apply 2D CNN on projected feature images or apply 1D convolutional layers
directly on raw point sets. These methods cannot adequately recognize
fine-grained local structures caused by the uneven density distribution of the
point cloud data. In this paper, to address this challenging issue, we
introduced a density-aware convolution module which uses the point-wise density
to re-weight the learnable weights of convolution kernels. The proposed
convolution module is able to fully approximate the 3D continuous convolution
on unevenly distributed 3D point sets. Based on this convolution module, we
further developed a multi-scale fully convolutional neural network with
downsampling and upsampling blocks to enable hierarchical point feature
learning. In addition, to regularize the global semantic context, we
implemented a context encoding module to predict a global context encoding and
formulated a context encoding regularizer to enforce the predicted context
encoding to be aligned with the ground truth one. The overall network can be
trained in an end-to-end fashion with the raw 3D coordinates as well as the
height above ground as inputs. Experiments on the International Society for
Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark demonstrated
the superiority of the proposed method for point cloud classification. Our
model achieved a new state-of-the-art performance with an average F1 score of
71.2% and improved the performance by a large margin on several categories
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