Non-local-means image denoising is based on processing a set of neighbors for a given reference patch. few nearest neighbors (NN) can be used to limit the computational burden of the algorithm. Resorting to a toy problem, we show analytically that sampling neighbors with the NN approach introduces a bias in the denoised patch. We propose a different neighbors’ collection criterion to alleviate this issue, which we name statistical NN (SNN). Our approach outperforms the traditional one in case of both white and colored noise: fewer SNNs can be used to generate images of superior quality, at a lower computational cost. A detailed investigation of our toy problem explains the differences between NN and SNN from a grounded point of view. The intuition behind SNN is quite general, and it leads to image quality improvement also in the case of bilateral filtering. The MATLAB code to replicate the results presented in the paper is freely available at https://github.com/NVlabs/SNN.

Fully autonomous vehicles (AVs), i.e., AVs with autonomy level 5, are expected to dominate road transportation in the near future and contribute trillions of dollars to the global economy. The general public, government organizations, and manufacturers all have significant concern regarding resiliency and safety standards of the autonomous driving system (ADS) of AVs.

Using synthetic data for training deep neural networks for robotic manipulation holds the promise of an almost unlimited amount of pre-labeled training data, generated safely out of harm's way. One of the key challenges of synthetic data, to date, has been to bridge the so-called \emph{reality gap}, so that networks trained on synthetic data operate correctly when exposed to real-world data. We explore the reality gap in the context of 6-DoF pose estimation of known objects from a single RGB image.

Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks. While various superpixel computation models exist, they are not differentiable, making them difficult to integrate into otherwise end-to-end trainable deep neural networks. In this work, we develop a new differentiable model for superpixel sampling that better leverages deep networks for learning superpixel segmentation.

Application execution on safety-critical and high-performance computer systems must be resilient to transient errors. As GPUs become more pervasive in such systems, they must supplement ECC/parity for major storage structures with reliability techniques that cover more of the GPU hardware logic. Instruction duplication has been explored for CPU resilience; however, it has never been studied in the context of GPUs, and it is unclear whether the performance and design choices it presents make it a feasible GPU solution.

The design challenges of see-through near-eye displays can be mitigated by specializing an augmented reality device for a particular application. We present a novel optical design for augmented reality near-eye displays exploiting 3D stereolithography printing techniques to achieve similar characteristics to progressive prescription binoculars. We propose to manufacture inter-changeable optical components using 3D printing, leading to arbitrary shaped static projection screen surfaces that are adaptive to the targeted applications.

Geometric aliasing is a persistent challenge for real-time rendering. Hardware multisampling remains limited to 8 × , analytic coverage fails to capture correlated visibility samples, and spatial and temporal postfiltering primarily target edges of superpixel primitives. We describe a novel semi-analytic representation of coverage designed to make progress on geometric antialiasing for subpixel primitives and pixels containing many edges while handling correlated subpixel coverage.