4,600 research outputs found
3D scanning of cultural heritage with consumer depth cameras
Three dimensional reconstruction of cultural heritage objects is an expensive and time-consuming process. Recent consumer real-time depth acquisition devices, like Microsoft Kinect, allow very fast and simple acquisition of 3D views. However 3D scanning with such devices is a challenging task due to the limited accuracy and reliability of the acquired data. This paper introduces a 3D reconstruction pipeline suited to use consumer depth cameras as hand-held scanners for cultural heritage objects. Several new contributions have been made to achieve this result. They include an ad-hoc filtering scheme that exploits the model of the error on the acquired data and a novel algorithm for the extraction of salient points exploiting both depth and color data. Then the salient points are used within a modified version of the ICP algorithm that exploits both geometry and color distances to precisely align the views even when geometry information is not sufficient to constrain the registration. The proposed method, although applicable to generic scenes, has been tuned to the acquisition of sculptures and in this connection its performance is rather interesting as the experimental results indicate
Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline
In this paper, we show how absolute orientation measurements provided by
low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion
pipeline. We show that integration improves both runtime, robustness and
quality of the 3D reconstruction. In particular, we use this orientation data
to seed and regularize the ICP registration technique. We also present a
technique to filter the pairs of 3D matched points based on the distribution of
their distances. This filter is implemented efficiently on the GPU. Estimating
the distribution of the distances helps control the number of iterations
necessary for the convergence of the ICP algorithm. Finally, we show
experimental results that highlight improvements in robustness, a speed-up of
almost 12%, and a gain in tracking quality of 53% for the ATE metric on the
Freiburg benchmark.Comment: CVPR Workshop on Visual Odometry and Computer Vision Applications
Based on Location Clues 201
LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes
Deep neural network (DNN) architectures have been shown to outperform
traditional pipelines for object segmentation and pose estimation using RGBD
data, but the performance of these DNN pipelines is directly tied to how
representative the training data is of the true data. Hence a key requirement
for employing these methods in practice is to have a large set of labeled data
for your specific robotic manipulation task, a requirement that is not
generally satisfied by existing datasets. In this paper we develop a pipeline
to rapidly generate high quality RGBD data with pixelwise labels and object
poses. We use an RGBD camera to collect video of a scene from multiple
viewpoints and leverage existing reconstruction techniques to produce a 3D
dense reconstruction. We label the 3D reconstruction using a human assisted
ICP-fitting of object meshes. By reprojecting the results of labeling the 3D
scene we can produce labels for each RGBD image of the scene. This pipeline
enabled us to collect over 1,000,000 labeled object instances in just a few
days. We use this dataset to answer questions related to how much training data
is required, and of what quality the data must be, to achieve high performance
from a DNN architecture
Non-iterative RGB-D-inertial Odometry
This paper presents a non-iterative solution to RGB-D-inertial odometry
system. Traditional odometry methods resort to iterative algorithms which are
usually computationally expensive or require well-designed initialization. To
overcome this problem, this paper proposes to combine a non-iterative front-end
(odometry) with an iterative back-end (loop closure) for the RGB-D-inertial
SLAM system. The main contribution lies in the novel non-iterative front-end,
which leverages on inertial fusion and kernel cross-correlators (KCC) to match
point clouds in frequency domain. Dominated by the fast Fourier transform
(FFT), our method is only of complexity , where is
the number of points. Map fusion is conducted by element-wise operations, so
that both time and space complexity are further reduced. Extensive experiments
show that, due to the lightweight of the proposed front-end, the framework is
able to run at a much faster speed yet still with comparable accuracy with the
state-of-the-arts
Real-time High Resolution Fusion of Depth Maps on GPU
A system for live high quality surface reconstruction using a single moving
depth camera on a commodity hardware is presented. High accuracy and real-time
frame rate is achieved by utilizing graphics hardware computing capabilities
via OpenCL and by using sparse data structure for volumetric surface
representation. Depth sensor pose is estimated by combining serial texture
registration algorithm with iterative closest points algorithm (ICP) aligning
obtained depth map to the estimated scene model. Aligned surface is then fused
into the scene. Kalman filter is used to improve fusion quality. Truncated
signed distance function (TSDF) stored as block-based sparse buffer is used to
represent surface. Use of sparse data structure greatly increases accuracy of
scanned surfaces and maximum scanning area. Traditional GPU implementation of
volumetric rendering and fusion algorithms were modified to exploit sparsity to
achieve desired performance. Incorporation of texture registration for sensor
pose estimation and Kalman filter for measurement integration improved accuracy
and robustness of scanning process
3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems
The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors' measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds' alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments
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