03-epipolar-geometry.pdf
web.stanford.edu · 4,103 words · saved by 1 readers
N/A
CS231A Course Notes 3: Epipolar Geometry Kenji Hata and Silvio Savarese 1 Introduction Previously, we have seen how to compute the intrinsic and extrinsic param- eters of a camera using one or more views using a typical camera calibration procedure or single view metrology. This process culminated in deriving properties about the 3D world from one image. However, in general, it is not possible to recover the entire structure of the 3D world from just one image. This is due to the intrinsic ambiguity of the 3D to the 2D mapping: some information is simply lost.…
related reading
- 01-camera-models.pdfweb.stanford.edu
- Perspective-n-Point - Wikipediaen.wikipedia.org
- DUSt3R: Geometric 3D Vision Made Easyarxiv.org
- 3D-Aware Ellipse Prediction for Object-Based Camera Pose Estimationarxiv.org
- Introduction to Inverse Problemsstat.uchicago.edu
- Homography - Wikipediaen.wikipedia.org
- 3D Object Detection via 2D Segmentation-Based Computational Integral Imaging Applied to a Real Videoncbi.nlm.nih.gov
- mls.pdfcs.harvard.edu
- CS231n Deep Learning for Computer Visioncs231n.github.io
- S4Darxiv.org
- Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Imagescis.temple.edu
- An Elementary Introduction to Information Geometryfranknielsen.github.io