34,442 research outputs found
Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point Cloud Compression
Real-time light detection and ranging (LiDAR) perceptions, e.g., 3D object
detection and simultaneous localization and mapping are computationally
intensive to mobile devices of limited resources and often offloaded on the
edge. Offloading LiDAR perceptions requires compressing the raw sensor data,
and lossy compression is used for efficiently reducing the data volume. Lossy
compression degrades the quality of LiDAR point clouds, and the perception
performance is decreased consequently. In this work, we present an
interpolation algorithm improving the quality of a LiDAR point cloud to
mitigate the perception performance loss due to lossy compression. The
algorithm targets the range image (RI) representation of a point cloud and
interpolates points at the RI based on depth gradients. Compared to existing
image interpolation algorithms, our algorithm shows a better qualitative result
when the point cloud is reconstructed from the interpolated RI. With the
preliminary results, we also describe the next steps of the current work.Comment: extended abstract of 2 pages, 2 figures, 1 tabl
Interpolation routines assessment in ALS-derived Digital Elevation Models for forestry applications
Airborne Laser Scanning (ALS) is capable of estimating a variety of forest parameters using different metrics extracted from the normalized heights of the point cloud using a Digital Elevation Model (DEM). In this study, six interpolation routines were tested over a range of land cover and terrain roughness in order to generate a collection of DEMs with spatial resolution of 1 and 2 m. The accuracy of the DEMs was assessed twice, first using a test sample extracted from the ALS point cloud, second using a set of 55 ground control points collected with a high precision Global Positioning System (GPS). The effects of terrain slope, land cover, ground point density and pulse penetration on the interpolation error were examined stratifying the study area with these variables. In addition, a Classification and Regression Tree (CART) analysis allowed the development of a prediction uncertainty map to identify in which areas DEMs and Airborne Light Detection and Ranging (LiDAR) derived products may be of low quality. The Triangulated Irregular Network (TIN) to raster interpolation method produced the best result in the validation process with the training data set while the Inverse Distance Weighted (IDW) routine was the best in the validation with GPS (RMSE of 2.68 cm and RMSE of 37.10 cm, respectively)
PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention
A point cloud is an agile 3D representation, efficiently modeling an object's
surface geometry. However, these surface-centric properties also pose
challenges on designing tools to recognize and synthesize point clouds. This
work presents a novel autoregressive model, PointGrow, which generates
realistic point cloud samples from scratch or conditioned on given semantic
contexts. Our model operates recurrently, with each point sampled according to
a conditional distribution given its previously-generated points. Since point
cloud object shapes are typically encoded by long-range interpoint
dependencies, we augment our model with dedicated self-attention modules to
capture these relations. Extensive evaluation demonstrates that PointGrow
achieves satisfying performance on both unconditional and conditional point
cloud generation tasks, with respect to fidelity, diversity and semantic
preservation. Further, conditional PointGrow learns a smooth manifold of given
image conditions where 3D shape interpolation and arithmetic calculation can be
performed inside
Surface Reconstruction from Scattered Point via RBF Interpolation on GPU
In this paper we describe a parallel implicit method based on radial basis
functions (RBF) for surface reconstruction. The applicability of RBF methods is
hindered by its computational demand, that requires the solution of linear
systems of size equal to the number of data points. Our reconstruction
implementation relies on parallel scientific libraries and is supported for
massively multi-core architectures, namely Graphic Processor Units (GPUs). The
performance of the proposed method in terms of accuracy of the reconstruction
and computing time shows that the RBF interpolant can be very effective for
such problem.Comment: arXiv admin note: text overlap with arXiv:0909.5413 by other author
- …