Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences

By Frank Steinbrücker, Christian Kerl, Daniel Cremers. 2013

184 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Geology

Citations in the corpus

OctNetFusion: Learning Depth Fusion from Data (2017, methodology): “While various weight profiles have been proposed [46], we found that the simple constant profile proposed by Newcombe et al.”
3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017, background): “Most of those approaches typically sample multiple depth images from different views of the object to create the complete 3D structure [37] [39] [53].”
Robust reconstruction of indoor scenes (2015, background): “A number of RGB-D reconstruction systems with integrated loop closure handling have been developed [29, 18, 28, 56, 62].”
ElasticFusion: Real-time dense SLAM and light source estimation (2016, result): “This is in contrast to other dense reconstruction systems which don’t strictly perform both tracking and mapping in real-time (Steinbrücker et al., 2013, 2014).”
An Octree-Based Approach towards Efficient Variational Range Data Fusion (2016, methodology): “We evaluate on various datasets and outline the suitability in terms of performance and geometric accuracy.”
3D Object Dense Reconstruction from a Single Depth View (2018, background): “Those approaches typically require multiple depth images from different viewing angles of an object to estimate the complete 3D structure [3] [4] [5].”
ElasticFusion: Dense SLAM Without A Pose Graph (2015, result): “This is in contrast to other dense reconstruction systems which don’t strictly perform both tracking and mapping in real-time [18, 19].”