Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2010-11-03
Proc. 13th International Conf. on Digital Image Computing: Techniques and Applications (DICTA-2011) 37--44
Computer Science
Computer Vision and Pattern Recognition
18 LaTeX pages, 7 figures
Scientific paper
The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision ranging from robotic vision to image analysis. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods: It does neither require prior training nor learning, nor knowledge of the camera parameters, nor explicit point correspondences or matching features between image and model. Unlike techniques that estimate a partial 3D pose (as in an overhead view of traffic or machine parts on a conveyor belt), our method estimates the complete 3D pose of the object, and works on a single static image from a given view, and under varying and unknown lighting conditions. For this purpose we derive a novel illumination-invariant distance measure between 2D photo and projected 3D model, which is then minimised to find the best pose parameters. Results for vehicle pose detection are presented.
Brewer Nathan
Hutter Marcus
Jayawardena Srimal
No associations
LandOfFree
Featureless 2D-3D Pose Estimation by Minimising an Illumination-Invariant Loss does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Featureless 2D-3D Pose Estimation by Minimising an Illumination-Invariant Loss, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Featureless 2D-3D Pose Estimation by Minimising an Illumination-Invariant Loss will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-145171