In this report, a fast high-precision stereo vision engine for implementation on an FPGA is studied. Different aspects of stereo vision and obstacles within this area are explored. Two stereo vision al- gorithms: Efficient Edge Preserving Stereo Matching and Fast Cost-Volume Matching have been studied and implemented in Python. A discussion concerning the al- gorithms computational complexity and stereo matching quality results in the choice of Efficient Edge Preserving Stereo Matching for further implementation. A fi- nal implementation was not achieved but the challenges of implementing an expo- nential function and the challenges with memory usage with large images were studied and solutions were found.