Improved Census Transforms for Resource-Optimized Stereo Vision

Abstract

Real-time stereo vision has proven to be a useful technology with many applications. However, the computationally intensive nature of stereo vision algorithms makes real-time implementation difficult in resource-limited systems. The field-programmable gate array (FPGA) has proven to be very useful in the implementation of local stereo methods, yet the resource requirements can still be a significant challenge. This paper proposes a variety of sparse census transforms that dramatically reduce the resource requirements of census-based stereo systems while maintaining stereo correlation accuracy. This paper also proposes and analyzes a new class of census-like transforms, called the generalized census transforms. This new transform allows a variety of very sparse census-like stereo correlation algorithms to be implemented while demonstrating increased robustness and flexibility. The resource savings and performance of these transforms is demonstrated by the design and implementation of a parameterizable stereo system that can implement stereo correlation using any census transform. Several optimizations for typical FPGA-based correlation systems are also proposed. The resulting system is capable of running at over 500 MHz on a modern FPGA, resulting in a throughput of over 500 million input pixel pairs per second.