1,923 research outputs found
Creating Simplified 3D Models with High Quality Textures
This paper presents an extension to the KinectFusion algorithm which allows
creating simplified 3D models with high quality RGB textures. This is achieved
through (i) creating model textures using images from an HD RGB camera that is
calibrated with Kinect depth camera, (ii) using a modified scheme to update
model textures in an asymmetrical colour volume that contains a higher number
of voxels than that of the geometry volume, (iii) simplifying dense polygon
mesh model using quadric-based mesh decimation algorithm, and (iv) creating and
mapping 2D textures to every polygon in the output 3D model. The proposed
method is implemented in real-time by means of GPU parallel processing.
Visualization via ray casting of both geometry and colour volumes provides
users with a real-time feedback of the currently scanned 3D model. Experimental
results show that the proposed method is capable of keeping the model texture
quality even for a heavily decimated model and that, when reconstructing small
objects, photorealistic RGB textures can still be reconstructed.Comment: 2015 International Conference on Digital Image Computing: Techniques
and Applications (DICTA), Page 1 -
Polylidar3D -- Fast Polygon Extraction from 3D Data
Flat surfaces captured by 3D point clouds are often used for localization,
mapping, and modeling. Dense point cloud processing has high computation and
memory costs making low-dimensional representations of flat surfaces such as
polygons desirable. We present Polylidar3D, a non-convex polygon extraction
algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data),
organized point clouds (e.g., range images), or user-provided meshes.
Non-convex polygons represent flat surfaces in an environment with interior
cutouts representing obstacles or holes. The Polylidar3D front-end transforms
input data into a half-edge triangular mesh. This representation provides a
common level of input data abstraction for subsequent back-end processing. The
Polylidar3D back-end is composed of four core algorithms: mesh smoothing,
dominant plane normal estimation, planar segment extraction, and finally
polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU
multi-threading and GPU acceleration when available. We demonstrate
Polylidar3D's versatility and speed with real-world datasets including aerial
LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds
for road surface detection, and RGBD cameras for indoor floor/wall detection.
We also evaluate Polylidar3D on a challenging planar segmentation benchmark
dataset. Results consistently show excellent speed and accuracy.Comment: 40 page
The AAU Multimodal Annotation Toolboxes: Annotating Objects in Images and Videos
This tech report gives an introduction to two annotation toolboxes that
enable the creation of pixel and polygon-based masks as well as bounding boxes
around objects of interest. Both toolboxes support the annotation of sequential
images in the RGB and thermal modalities. Each annotated object is assigned a
classification tag, a unique ID, and one or more optional meta data tags. The
toolboxes are written in C++ with the OpenCV and Qt libraries and are operated
by using the visual interface and the extensive range of keyboard shortcuts.
Pre-built binaries are available for Windows and MacOS and the tools can be
built from source under Linux as well. So far, tens of thousands of frames have
been annotated using the toolboxes.Comment: 6 pages, 10 figure
3-D model construction using range and image data
This paper deals with the automated creation of geometric and photometric correct 3-D models of the world. Those models can be used for virtual reality, tele-presence, digital cinematography and urban planning applications. The combination of range (dense depth estimates) and image sensing (color information) provides data-sets which allow us to create geometrically correct, photorealistic models of high quality. The 3-D models are first built from range data using a volumetric set intersection method previously developed by us. Photometry can be mapped onto these models by registering features from both the 3-D and 2-D data sets. Range data segmentation algorithms have been developed to identify planar regions, determine linear features from planar intersections that can serve as features for registration with 2-D imagery lines, and reduce the overall complexity of the models. Results are shown for building models of large buildings on our campus using real data acquired from multiple sensors
Structured Indoor Modeling
In this dissertation, we propose data-driven approaches to reconstruct 3D models for indoor scenes which are represented in a structured way (e.g., a wall is represented by a planar surface and two rooms are connected via the wall). The structured representation of models is more application ready than dense representations (e.g., a point cloud), but poses additional challenges for reconstruction since extracting structures requires high-level understanding about geometries. To address this challenging problem, we explore two common structural regularities of indoor scenes: 1) most indoor structures consist of planar surfaces (planarity), and 2) structural surfaces (e.g., walls and floor) can be represented by a 2D floorplan as a top-down view projection (orthogonality). With breakthroughs in data capturing techniques, we develop automated systems to tackle structured modeling problems, namely piece-wise planar reconstruction and floorplan reconstruction, by learning shape priors (i.e., planarity and orthogonality) from data. With structured representations and production-level quality, the reconstructed models have an immediate impact on many industrial applications
PlaneSLAM: Plane-based LiDAR SLAM for Motion Planning in Structured 3D Environments
LiDAR sensors are a powerful tool for robot simultaneous localization and
mapping (SLAM) in unknown environments, but the raw point clouds they produce
are dense, computationally expensive to store, and unsuited for direct use by
downstream autonomy tasks, such as motion planning. For integration with motion
planning, it is desirable for SLAM pipelines to generate lightweight geometric
map representations. Such representations are also particularly well-suited for
man-made environments, which can often be viewed as a so-called "Manhattan
world" built on a Cartesian grid. In this work we present a 3D LiDAR SLAM
algorithm for Manhattan world environments which extracts planar features from
point clouds to achieve lightweight, real-time localization and mapping. Our
approach generates plane-based maps which occupy significantly less memory than
their point cloud equivalents, and are suited towards fast collision checking
for motion planning. By leveraging the Manhattan world assumption, we target
extraction of orthogonal planes to generate maps which are more structured and
organized than those of existing plane-based LiDAR SLAM approaches. We
demonstrate our approach in the high-fidelity AirSim simulator and in
real-world experiments with a ground rover equipped with a Velodyne LiDAR. For
both cases, we are able to generate high quality maps and trajectory estimates
at a rate matching the sensor rate of 10 Hz
Combining Occupancy Grids with a Polygonal Obstacle World Model for Autonomous Flights
This chapter presents a mapping process that can be applied to autonomous systems for obstacle avoidance and trajectory planning. It is an improvement over commonly applied obstacle mapping techniques, such as occupancy grids. Problems encountered in large outdoor scenarios are tackled and a compressed map that can be sent on low-bandwidth networks is produced. The approach is real-time capable and works in full 3-D environments. The
efficiency of the proposed approach is demonstrated under real operational conditions on an unmanned aerial vehicle using stereo vision for distance measurement
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