The 3D Non-Local Means (NLM) algorithm has become a crucial preprocessing technique for 3D image datasets due to its effectiveness in denoising while preserving fine details. This method has been proven to be highly efficient in high-demand tasks within industrial applications such as medical imaging and remote sensing. The 3D NLM algorithm computes the filtered value for each voxel by calculating the weighted average of all voxels within a 3D search window, where the weights are determined by the similarity between pairs of 3D template windows. Therefore, the computational burden becomes significant, especially in embedded GPUs with limited computational power and memory resources. To address this issue, we propose an efficient GPU parallel kernel to minimize redundant computations and memory accesses. The kernel integrates three nested reuse strategies to handle redundant computations in three dimensions: for columns, we leverage the fast data exchange mechanism to reuse column computation results via on-chip registers; for rows, we use a sliding window strategy, utilizing GPU global memory as an intermediary to store and reuse similarity values between filtered rows; and for channels, we introduce a zigzag scanning strategy that enables simultaneous computation across multiple channels and employs on-chip registers to facilitate channel computation reuse. Experimental results demonstrate that our kernel achieves an average speedup of 7.7x on the embedded Jetson AGX Xavier platform across a range of 3D image datasets compared to existing methods, showcasing exceptional performance.

The visual-based SLAM (Simultaneous Localization and Mapping) is a technology widely used in applications such as robotic navigation and virtual reality, which primarily focuses on detecting feature points from visual images to construct an unknown environmental map and simultaneously determines its own location. It usually imposes stringent requirements on hardware power consumption, processing speed, and accuracy. Currently, the ORB (Oriented FAST and Rotated BRIEF)-based SLAM systems have exhibited superior performance in terms of processing speed and robustness. However, they still fall short of meeting the demands for real-time processing on mobile platforms. This limitation is primarily due to the time-consuming Oriented FAST calculations accounting for approximately half of the entire SLAM system. This article presents two methods to accelerate the Oriented FAST feature detection on low-end embedded GPUs. These methods optimize the most time-consuming steps in Oriented FAST feature detection: FAST feature point detection and Harris corner detection, which is achieved by implementing a binary-level encoding strategy to determine candidate points quickly and a separable Harris detection strategy with efficient low-level GPU hardware-specific instructions. Extensive experiments on a Jetson TX2 embedded GPU demonstrate an average speedup of over 7.3 times compared to widely used OpenCV with GPU support. This significant improvement highlights its effectiveness and potential for real-time applications in mobile and resource-constrained environments.

The GIST descriptor is a classic feature descriptor primarily used for scene categorization and recognition tasks. It drives a bank of Gabor filters, which respond to edges and textures at various scales and orientations to capture the spatial structures in an image. Compared to other scene recognition algorithms that rely on detailed object detection, GIST has lower computational complexity, allowing it to be widely applied. However, its internal multi-scale and multi-orientation Gabor filters also mean that systems based on it cannot be executed fast enough. This article proposes an optimized GPU kernel for the GIST descriptor. It fully takes advantage of the symmetry of Gabor filters and proposes different optimization strategies for both oblique and orthogonal orientations. Extensive experiments demonstrate that the proposed kernel is adaptable to images of various scales and different GPUs. Compared to the cuFFT library, our kernel achieves 12.09× and 3.86× acceleration on an RTX 3080 GPU and a Jetson AGX Xavier GPU, respectively.

We propose a lightweight system for stereo-matching using embedded graphic processing units (GPUs). The proposed system overcomes the trade-off between accuracy and processing speed in stereo matching, thus further improving the matching accuracy while ensuring real-time processing. The basic idea is to construct a tiny neural network based on a variational autoencoder (VAE) to achieve the upscaling and refinement a small size of coarse disparity map. This map is initially generated using a traditional matching method. The proposed hybrid structure maintains the advantage of low computational complexity found in traditional methods. Additionally, it achieves matching accuracy with the help of a neural network. Extensive experiments on the KITTI 2015 benchmark dataset demonstrate that our tiny system exhibits high robustness in improving the accuracy of coarse disparity maps generated by different algorithms, while running in real-time on embedded GPUs.