Compressed Voxel-Based Mapping Using Unsupervised Learning

By Daniel Ricao Canelhas, Erik Schaffernicht, Todor Stoyanov, Achim J. Lilienthal, Andrew J. Davison. 2016

11 citations recorded by the source corpus.

In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance field ...

Subfields: Aerospace Engineering, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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

1. Peter R. Florence
2. Steven J. Lovegrove
3. Julian Straub
4. Richard A. Newcombe
5. J. Park
6. Andrew J. Davison
7. Daniel Cremers
8. Dieter Fox

In Computer Graphics and Computer-Aided Design

1. Andreas Geiger
2. Julian Straub
3. J. Park
4. Steven J. Lovegrove
5. Peter R. Florence
6. Richard A. Newcombe
7. Michael Niemeyer
8. Gordon Wetzstein

In Computer Vision and Pattern Recognition

1. Andreas Geiger
2. Peter R. Florence
3. Andrew J. Davison
4. Luc Van Gool
5. Steven J. Lovegrove
6. Jitendra Malik
7. Julian Straub
8. Richard A. Newcombe

Citations in the corpus

• DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation (2019, methodology): “[30] reports various wavelet transform-based approaches for distance field compression, while [10] applies dimensionality reduction techniques on discrete TSDF volumes.”