Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements

We present an approach to depth estimation that fuses information from a stereo pair with sparse range measurements derived from a LIDAR sensor or a range camera. The goal of this work is to exploit the complementary strengths of the two sensor modalities, the accurate but sparse range measurements and the ambiguous but dense stereo information. These two sources are effectively and efficiently fused by combining ideas from anisotropic diffusion and semi-global matching. We evaluate our approach on the KITTI 2015 and Middlebury 2014 datasets, using randomly sampled ground truth range measurements as our sparse depth input. We achieve significant performance improvements with a small fraction of range measurements on both datasets. We also provide qualitative results from our platform using the PMDTec Monstar sensor. Our entire pipeline runs on an NVIDIA TX-2 platform at 5Hz on 1280x1024 stereo images with 128 disparity levels.Comment: 7 pages, 5 figures, 2 table

R3^3SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems

Stereo depth estimation is used for many computer vision applications. Though many popular methods strive solely for depth quality, for real-time mobile applications (e.g. prosthetic glasses or micro-UAVs), speed and power efficiency are equally, if not more, important. Many real-world systems rely on Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but power efficiency is hard to achieve with conventional hardware, making the use of embedded devices such as FPGAs attractive for low-power applications. However, the full SGM algorithm is ill-suited to deployment on FPGAs, and so most FPGA variants of it are partial, at the expense of accuracy. In a non-FPGA context, the accuracy of SGM has been improved by More Global Matching (MGM), which also helps tackle the streaking artifacts that afflict SGM. In this paper, we propose a novel, resource-efficient method that is inspired by MGM's techniques for improving depth quality, but which can be implemented to run in real time on a low-power FPGA. Through evaluation on multiple datasets (KITTI and Middlebury), we show that in comparison to other real-time capable stereo approaches, we can achieve a state-of-the-art balance between accuracy, power efficiency and speed, making our approach highly desirable for use in real-time systems with limited power.Comment: Accepted in FPT 2018 as Oral presentation, 8 pages, 6 figures, 4 table

Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices

For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts. Their biggest drawback, however, is that they do not directly sense depth maps; instead, these must be estimated through data-intensive processes. Therefore, appropriate algorithm selection plays an important role in achieving the desired performance characteristics. Motivated by applications in space and mobile robotics, we implement and evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering one of the best trade-offs between efficiency and accuracy, ELAS has only been shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all intriguing properties of the original algorithm, such as the slanted plane priors, but can achieve a frame rate of 47fps whilst consuming under 4W of power. Unlike previous FPGA based designs, we take advantage of both components on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate more complex and computationally diverse algorithms for such low power, real-time systems.Comment: 8 pages, 7 figures, 2 table

FPGA-based multi-view stereo system with flexible measurement setup

In recent years, stereoscopic image processing algorithms have gained importance for a variety of applications. To capture larger measurement volumes, multiple stereo systems are combined into a multi-view stereo (MVS) system. To reduce the amount of data and the data rate, calculation steps close to the sensors are outsourced to Field Programmable Gate Arrays (FPGAs) as upstream computing units. The calculation steps include lens distortion correction, rectification and stereo matching. In this paper a FPGA-based MVS system with flexible camera arrangement and partly overlapping field of view is presented. The system consists of four FPGA-based passive stereoscopic systems (Xilinx Zynq-7000 7020 SoC, EV76C570 CMOS sensor) and a downstream processing unit (Zynq Ultrascale ZU9EG SoC). This synchronizes the sensor near processing modules and receives the disparity maps with corresponding left camera image via HDMI. The subsequent computing unit calculates a coherent 3D point cloud. Our developed FPGA-based 3D measurement system captures a large measurement volume at 24 fps by combining a multiple view with eight cameras (using Semi-Global Matching for an image size of 640 px × 460 px, up to 256 px disparity range and with aggregated costs over 4 directions). The capabilities and limitation of the system are shown by an application example with optical non-cooperative surface

Fast Stereo Disparity Maps Refinement By Fusion of Data-Based And Model-Based Estimations

International audienceThe estimation of disparity maps from stereo pairs has many applications in robotics and autonomous driving. Stereo matching has first been solved using model-based approaches, with real-time considerations for some, but to-day's most recent works rely on deep convolutional neural networks and mainly focus on accuracy at the expense of computing time. In this paper, we present a new method for disparity maps estimation getting the best of both worlds: the accuracy of data-based methods and the speed of fast model-based ones. The proposed approach fuses prior disparity maps to estimate a refined version. The core of this fusion pipeline is a convolutional neural network that leverages dilated convolutions for fast context aggregation without spatial resolution loss. The resulting architecture is both very effective for the task of refining and fusing prior disparity maps and very light, allowing our fusion pipeline to produce disparity maps at rates up to 125 Hz. We obtain state-of-the-art results in terms of speed and accuracy on the KITTI benchmarks. Code and pre-trained models are available on our github: https://github.com/ ferreram/FD-Fusion