  This project aims to create 3d model of the natural world and model changes
in it instantaneously. A framework for modeling instantaneous changes natural
scenes in real time using Lagrangian Particle Framework and a fluid-particle
grid approach is presented. This project is presented in the form of a
proof-based system where we show that the design is very much possible but
currently we only have selective scripts that accomplish the given job, a
complete software however is still under work. This research can be divided
into 3 distinct sections: the first one discusses a multi-camera rig that can
measure ego-motion accurately up to 88%, how this device becomes the backbone
of our framework, and some improvements devised to optimize a know framework
for depth maps and 3d structure estimation from a single still image called
make3d. The second part discusses the fluid-particle framework to model natural
scenes, presents some algorithms that we are using to accomplish this task and
we show how an application of our framework can extend make3d to model natural
scenes in real time. This part of the research constructs a bridge between
computer vision and computer graphics so that now ideas, answers and intuitions
that arose in the domain of computer graphics can now be applied to computer
vision and natural modeling. The final part of this research improves upon what
might become the first general purpose vision system using deep belief
architectures and provides another framework to improve the lower bound on
training images for boosting by using a variation of Restricted Boltzmann
machines (RBM). We also discuss other applications that might arise from our
work in these areas.
