Trinocular Adaptive Window Size Disparity Estimation Algorithm and Its Real-Time Hardware

This paper proposes a hardware-oriented trinocular adaptive window size disparity estimation (T-AWDE) algorithm and the first real-time trinocular disparity estimation (DE) hardware that targets high-resolution images with high-quality disparity results. The proposed trinocular DE hardware is the enhanced version of the recently published binocular AWDE implementation. The T-AWDE hardware generates a very high quality depth map by merging two depth maps obtained from the center-left and center-right camera pairs. The T-AWDE hardware enhances disparity results by applying a double checking scheme which solves most of the occlusion problems existing in the AWDE implementation while providing correct disparity results even for objects located at left or right edge of the center image. The proposed T-AWDE hardware architecture enables handling 55 frames per second on a Virtex-7 FPGA at a 1024×768 XGA video resolution for a 128 pixels disparity range.

Real-Time High-Resolution Multiple-Camera Depth Map Estimation Hardware and Its Applications

Abdulkadir Akin

Depth information is used in a variety of 3D based signal processing applications such as autonomous navigation of robots and driving systems, object detection and tracking, computer games, 3D television, and free view-point synthesis. These applications require high accuracy and speed performances for depth estimation. Depth maps can be generated using disparity estimation methods, which are obtained from stereo matching between multiple images. The computational complexity of disparity estimation algorithms and the need of large size and bandwidth for the external and internal memory make the real-time processing of disparity estimation challenging, especially for high resolution images. This thesis proposes a high-resolution high-quality multiple-camera depth map estimation hardware. The proposed hardware is verified in real-time with a complete system from the initial image capture to the display and applications. The details of the complete system are presented. The proposed binocular and trinocular adaptive window size disparity estimation algorithms are carefully designed to be suitable to real-time hardware implementation by allowing efficient parallel and local processing while providing high-quality results. The proposed binocular and trinocular disparity estimation hardware implementations can process 55 frames per second on a Virtex-7 FPGA at a 1024 x 768 XGA video resolution for a 128 pixel disparity range. The proposed binocular disparity estimation hardware provides best quality compared to existing real-time high-resolution disparity estimation hardware implementations. A novel compressed-look up table based rectification algorithm and its real-time hardware implementation are presented. The low-complexity decompression process of the rectification hardware utilizes a negligible amount of LUT and DFF resources of the FPGA while it does not require the existence of external memory. The first real-time high-resolution free viewpoint synthesis hardware utilizing three-camera disparity estimation is presented. The proposed hardware generates high-quality free viewpoint video in real-time for any horizontally aligned arbitrary camera positioned between the leftmost and rightmost physical cameras. The full embedded system of the depth estimation is explained. The presented embedded system transfers disparity results together with synchronized RGB pixels to the PC for application development. Several real-time applications are developed on a PC using the obtained RGB+D results. The implemented depth estimation based real-time software applications are: depth based image thresholding, speed and distance measurement, head-hands-shoulders tracking, virtual mouse using hand tracking and face tracking integrated with free viewpoint synthesis. The proposed binocular disparity estimation hardware is implemented in an ASIC. The ASIC implementation of disparity estimation imposes additional constraints with respect to the FPGA implementation. These restrictions, their implemented efficient solutions and the ASIC implementation results are presented. In addition, a very high-resolution (82.3 MP) 360°x90° omnidirectional multiple camera system is proposed. The hemispherical camera system is able to view the target locations close to horizontal plane with more than two cameras. Therefore, it can be used in high-resolution 360° depth map estimation and its applications in the future.

EPFL2015

Enhanced Omnidirectional Image Reconstruction Algorithm and its Real-Time Hardware

Hossein Afshari, Abdulkadir Akin, Yusuf Leblebici, Alexandre Schmid

Omnidirectional stereoscopy and depth estimation are complex problems of image processing to which the Panoptic camera offers a novel solution. The Panoptic camera is a biologically-inspired vision sensor made of multiple cameras. It is a polydioptric system mimicking the eyes of flying insects where multiple imagers, each with a distinct focal point, are distributed over a hemisphere. Recently, the omnidirectional image reconstruction algorithm (OIR) and its real-time hardware implementation have been proposed for the Panoptic camera. This paper presents an enhanced omnidirectional image reconstruction algorithm (EOIR) and its real-time implementation. The proposed EOIR algorithm provides improved realistic omnidirectional images and residuals compared to OIR. As a processing core of EOIR, 57% of the available slice resources in a Virtex 5 FPGA are consumed. The proposed platform provides the high bandwidth required to simultaneously process data originating from 40 cameras, and reconstruct omnidirectional images of 256x1024 pixels at 25 fps. This proposed hardware and algorithmic enhancements enable advanced real-time applications including omnidirectional image reconstruction, 3D model construction and depth estimation.

2012

A Hardware-Oriented Dynamically Adaptive Disparity Estimation Algorithm and its Real-Time Hardware

Abdulkadir Akin, Hüseyin Baris Atakan, Ipek Baz, Irem Boybat Kara, Yusuf Leblebici, Alexandre Schmid

The computational complexity of disparity estimation algorithms and the need of large size and bandwidth for the external and internal memory make the real-time processing of disparity estimation challenging, especially for High Resolution (HR) images. This paper proposes a hardware-oriented adaptive window size disparity estimation (AWDE) algorithm and its real time reconfigurable hardware implementation that targets HR video with high quality disparity results. The proposed algorithm is a hybrid solution involving the Sum of Absolute Differences and the Census cost computation methods to vote and select the best suitable disparity candidates. It utilizes a pixel intensity based refinement step to remove faulty disparity computations. The AWDE algorithm dynamically adapts the window size considering the local texture of the image to increase the disparity estimation quality. The proposed reconfigurable hardware of the AWDE algorithm enables handling 60 frames per second on Virtex-5 FPGA at a 1024×768 XGA video resolution for a 120 pixel disparity range.

2013

Real-Time High-Resolution Multiple-Camera Depth Map Estimation Hardware and Its Applications

Abdulkadir Akin

EPFL2015