Directly regressing 6 degrees of freedom for all the objects from a single RGB image is not well explored. Even end-to-end pose estimation approaches for a single object are inferior compared to state-of-the-art methods in terms of accuracy. Most 6D pose estimation frameworks are multi-stage relying on off-the-shelf deep networks for object and keypoint detection to establish correspondences between 3D object keypoints and 2D image locations. This is followed by applying a variant of a RANSAC-based Perspective-n-Point (PnP) followed by complex refinement operation. In this work, we propose a multi-object 6D pose estimation framework by enhancing the popular YOLOX object detector. The network is end-to-end trainable and detects each object along with its pose from a single RGB image without any additional post-processing. We show that by properly parameterizing the 6D pose and carefully designing the loss function, we can achieve state-of-the-art accuracy without further refinement or any intermediate representations. YOLO-6D-Pose achieves SOTA results on YCBV and LMO dataset, surpassing all existing monocular approaches. We systematically analyze various 6D augmentations to verify their correctness and propose a new translation augmentation for this task. The network does not rely on any correspondences and is independent of the CAD model during inference. Code is available at https://github.com/TexasInstruments/edgeai-yolox.

Traditionally, file systems were implemented as part of operating systems kernels, which provide a limited set of tools and facilities to a programmer. As the complexity of file systems grew, many new file systems began being developed in user space. Low performance is considered the main disadvantage of user-space file systems but the extent of this problem has never been explored systematically. As a result, the topic of user-space file systems remains rather controversial: while some consider user-space file systems a “toy” not to be used in production, others develop full-fledged production file systems in user space. In this article, we analyze the design and implementation of a well-known user-space file system framework, FUSE, for Linux. We characterize its performance and resource utilization for a wide range of workloads. We present FUSE performance and also resource utilization with various mount and configuration options, using 45 different workloads that were generated using Filebench on two different hardware configurations. We instrumented FUSE to extract useful statistics and traces, which helped us analyze its performance bottlenecks and present our analysis results. Our experiments indicate that depending on the workload and hardware used, performance degradation (throughput) caused by FUSE can be completely imperceptible or as high as −83%, even when optimized; and latencies of FUSE file system operations can be increased from none to 4× when compared to Ext4. On the resource utilization side, FUSE can increase relative CPU utilization by up to 31% and underutilize disk bandwidth by as much as −80% compared to Ext4, though for many data-intensive workloads the impact was statistically indistinguishable. Our conclusion is that user-space file systems can indeed be used in production (non-“toy”) settings, but their applicability depends on the expected workloads.

Advanced driver assistance systems (ADAS) are designed to increase driver's situational awareness and road safety by providing essential information, warning and automatic intervention to reduce the possibility/severity of an accident. Of the various types of ADAS modalities available, camera based ADAS are being widely adopted for their usefulness in varied applications, overall reliability and adaptability to new requirements. But camera based ADAS also represents a complex, high-performance, and low-power compute problem, requiring specialized solutions. This paper introduces a high performance front camera ADAS based on a small area, low power System-on-Chip (SoC) solution from Texas Instruments called Texas Instruments Driver Assist 3x (TDA3x). The paper illustrates compute capabilities of the device in implementation of a typical front camera ADAS. The paper also introduces key programming concepts related to heterogeneous programmable compute cores in the SoC and the software framework to use those cores in order to develop the front camera solutions. These aspects will be of interest not only to the ADAS developers but for computer vision and compute intensive embedded system development.

MPEG-1 layer-3, popularly known as MP3, has revolutionized the digital music domain. MP3 makes use of psychoacoustic modeling to achieve compression through the removal of perceptually irrelevant components of digital audio. The psychoacoustic model is the key element of perceptual coding and requires intensive FFT computation for calculating the frequency spectrum. This spectrum is used to compute masking thresholds. Thus, the original MP3 algorithm computes modified discrete cosine transform (MDCT) and FFT parallelly. The proposed algorithm is an alternative to this. We make use of complex modified discrete cosine transform (CMDCT) of the filter-bank outputs for generating MDCT coefficients as well as the frequency spectrum. This method requires fewer computations than the original method. A novel method of window switching, based on filter-bank output is used to simplify the overall algorithm. The proposed algorithm reduces the amount of computations for MP3 encoder while retaining the audio quality.