scholarly journals Fast Out‐of‐Core Octree Generation for Massive Point Clouds

2020 ◽  
Vol 39 (7) ◽  
pp. 155-167
Author(s):  
Markus Schütz ◽  
Stefan Ohrhallinger ◽  
Michael Wimmer
Keyword(s):  
Point Clouds ◽  
Massive Point

Error-aware construction and rendering of multi-scan panoramas from massive point clouds

2017 ◽  
Vol 157 ◽  
pp. 43-54 ◽  
Author(s):  
Marc Comino ◽  
Carlos Andújar ◽  
Antonio Chica ◽  
Pere Brunet
Keyword(s):  
Point Clouds ◽  
Massive Point

scholarly journals Stereo Model Selection and Point Cloud Filtering using an Out-of-Core Octree

2014 ◽  
Vol XL-3 ◽  
pp. 373-380 ◽  
Author(s):  
K. Wenzel ◽  
N. Haala ◽  
D. Fritsch
Keyword(s):  
Model Selection ◽  
Image Matching ◽  
Stereo Matching ◽  
Point Clouds ◽  
Outlier Rejection ◽  
Object Space ◽  
Massive Point ◽  
3D Data ◽  
Dense Image ◽  
Stereo Triangulation

Dense image matching methods enable the retrieval of dense surface information using any kind of imagery. The best quality can be achieved for highly overlapping datasets, which avoids occlusions and provides highly redundant observations. Thus, images are acquired close to each other. This leads to datasets with increasing size &ndash; especially when large scenes are captured. While image acquisition can be performed in relatively short time, more time is required for data processing due to the computational complexity of the involved algorithms. For the dense surface reconstruction task, <i>Multi-View Stereo</i> algorithms can be used – which are typically beneficial due to the efficiency of image matching on stereo models. Our dense image matching solution <i>SURE</i> uses such an approach, where the result of stereo matching is fused using a multi-stereo triangulation in order to exploit the available redundancy. One key challenge of such <i>Multi-View Stereo</i> methods is the selection of suitable stereo models, where object space information should be considered to avoid unnecessary processing. Subsequently, the dense image matching step provides up to one 3D point for each pixel, which leads to massive point clouds. This large amount of 3D data needs to be filtered and integrated efficiently in object space. Within this paper, we present an <i>out-of-core octree</i>, which enables neighborhood and overlap analysis between point clouds. It is used on low-resolution point clouds to support the stereo model selection. Also, this tree is designed for the processing of massive point clouds with low memory requirements and thus can be used to perform outlier rejection, redundancy removal and resampling.

scholarly journals Fast low-memory seamless photo blending on massive point clouds using a streaming framework

2011 ◽  
Vol 4 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Ruggero Pintus ◽  
Enrico Gobbetti ◽  
Marco Callieri
Keyword(s):  
Point Clouds ◽  
Massive Point

scholarly journals An efficient multi-resolution framework for high quality interactive rendering of massive point clouds using multi-way kd-trees

2012 ◽  
Vol 29 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Prashant Goswami ◽  
Fatih Erol ◽  
Rahul Mukhi ◽  
Renato Pajarola ◽  
Enrico Gobbetti
Keyword(s):  
Point Clouds ◽  
High Quality ◽  
Interactive Rendering ◽  
Massive Point

Fast exact shortest distance queries for massive point clouds

2016 ◽  
Vol 84 ◽  
pp. 28-37
Author(s):  
David Eriksson ◽  
Evan Shellshear
Keyword(s):  
Point Clouds ◽  
Massive Point ◽  
Shortest Distance
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