A combined texture-shape descriptor for enhanced 3D feature matching
By Federico Tombari, Samuele Salti, Luigi Di Stefano. 2011
329 citations recorded by the source corpus.
Motivated by the increasing availability of 3D sensors capable of delivering both shape and texture information, this paper presents a novel descriptor for feature matching in 3D data enriched with texture. The proposed approach stems from the theory of a recently proposed descriptor for 3D data which relies on shape only, and represents its generalization to the case of multiple cues associated with a 3D mesh. The proposed descriptor, dubbed CSHOT, is demonstrated to notably improve the accuracy of feature matching in challenging object recognition scenarios characterized by the presence of clutter and occlusions.
Subfields: Aerospace Engineering, 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
- Yongbin Sun
- Michael M. Bronstein
- Justin M. Solomon
- Sanjay E. Sarma
- Li He
- Ziwei Liu
- Yue Wang
- Hong Zhang
In Computer Vision and Pattern Recognition
- Yue Wang
- Justin M. Solomon
- Ziwei Liu
- Luc Van Gool
- Michael M. Bronstein
- Dacheng Tao
- Xiaogang Wang
- Bernard Ghanem
Citations in the corpus
- Dynamic Graph CNN for Learning on Point Clouds (2018, background): “2009, 2008a], and normal histograms [Tombari et al. 2011], to name a few.”
- M2DP: A novel 3D point cloud descriptor and its application in loop closure detection (2016, methodology): “SHOT [8] can be seen as a hybrid of signature and histogram methods.”