Using Photometric Invariants for 3D Object Recognition

Using Photometric Invariants for 3D Object Recognition

In this paper we describe a new efficient algorithm for recognizing 3D objects by combining photometric and geometric invariants. We derive some photometric properties that are invariant to the changes of illumination and to relative object motion with respect to the camera and/or the lighting source in 3D space. We show that recognition does not require a full constancy of colors; rather, it o...

Indexing without Invariants in 3D Object Recognition

ÐWe present a method of indexing three-dimensional objects from single two-dimensional images. Unlike most other methods to solve this problem, ours does not rely on invariant features. This allows a richer set of shape information to be used in the recognition process. We also suggest the kd-tree as an alternative indexing data structure to the standard hash table. This makes hypothesis recove...

A novel Local feature descriptor using the Mercator projection for 3D object recognition

Point cloud processing is a rapidly growing research area of computer vision. Introducing of cheap range sensors has made a great interest in the point cloud processing and 3D object recognition. 3D object recognition methods can be divided into two categories: global and local feature-based methods. Global features describe the entire model shape whereas local features encode the neighborhood ...

3D Recognition Using Affine Invariants

The size of 3D data stored around the web has become bigger. Therefore the development of recognition applications and retrieval systems of 3D models is important. This paper deals with invariants for 3D models recognition. Thus under general affine transform we propose in a first time determinants of three points to realize invariance under affinity. To solve starting point problem we needs Fo...

MORSE: An Architecture for 3D Object Recognition Based on Invariants