  We propose a novel Line based parameterization for category specific CAD
models. The proposed parameterization associates 3D category-specific CAD model
and object under consideration using a dictionary based RANSAC method that uses
object Viewpoints as prior and edges detected in the respective intensity image
of the scene. The association problem is posed as a classical Geometry problem
rather than being dataset driven, thus saving the time and labour that one
invests in annotating dataset to train Keypoint Network for different category
objects. Besides eliminating the need of dataset preparation, the approach also
speeds up the entire process as this method processes the image only once for
all objects, thus eliminating the need of invoking the network for every object
in an image across all images. A 3D-2D edge association module followed by a
resection algorithm for lines is used to recover object poses. The formulation
optimizes for shape and pose of the object, thus aiding in recovering object 3D
structure more accurately. Finally, a Factor Graph formulation is used to
combine object poses with camera odometry to formulate a SLAM problem.
