Name
Title | ||
|---|---|---|
Efficient object categorization with the surface-approximation polynomials descriptor |
Abstract | ||
|---|---|---|
Perception of object categories is a key functionality towards more versatile autonomous robots. Object categorization enables robots to understand their environments even if certain instances of objects have never been seen before. In this paper we present the novel descriptor Surface-Approximation Polynomials (SAP) that directly computes a global description on point cloud surfaces of objects based on polynomial approximations of surface cuts. This descriptor is directly applicable to point clouds captured with time-of-flight or other depth sensors without any data preprocessing or normal computation. Hence, it is generated very fast. Together with a preceding pose normalization, SAP is invariant to scale and partially invariant to rotations. We demonstrate experiments in which SAP categorizes 78 % of test objects correctly while needing only 57 ms for the computation. This way SAP is superior to GFPFH, GRSD and VFH according to both criteria. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-32732-2_3 | Spatial Cognition |
Keywords | Field | DocType |
certain instance,object categorization,surface-approximation polynomials descriptor,point cloud surface,normal computation,test object,sap categorizes,efficient object categorization,global description,novel descriptor surface-approximation polynomials,object category,depth sensor | Computer vision,Categorization,Normalization (statistics),Polynomial,Data pre-processing,Invariant (mathematics),Artificial intelligence,Robot,Point cloud,Mathematics,Computation | Conference |
Citations | PageRank | References |
2 | 0.38 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richard Bormann | 1 | 43 | 6.01 |
| Jan Fischer | 2 | 2 | 0.38 |
| Georg Arbeiter | 3 | 56 | 7.09 |
| Alexander Verl | 4 | 167 | 50.15 |