This paper presents a new method to synthesize an image from the arbitrary view and time given a collection of images of a dynamic scene. A key challenge for the synthesis arises from dynamic scene reconstruction where epipolar geometry does not apply to the local motion of dynamic contents. Our insight is that although its scale and quality is inconsistent with other views, the depth estimation from a single view can be used to reason about the geometry of the local motion.

In this paper, we present a fully-customized AR display design that considers the user’s prescription, interpupillary distance, and taste of fashion. A free-form image combiner embedded inside the prescription lens provides augmented images onto the vision-corrected real world. The optics was optimized for each prescription level, which can reduce the mass production cost while satisfying the user’s taste. The foveated optimization method was applied which distributes the pixels in accordance with human visual acuity.

Emerging fields of mixed reality and electronic sports necessitate greater spatial and temporal resolutions in displays. We introduce a novel scanning display method that enhances spatiotemporal qualities of displays. Specifically, we demonstrate that scanning multiple image patches that are representing basis functions of each block in a target image can help to synthesize spatiotemporally enhanced visuals. To discover the right image patches, we introduce an optimization framework tailored to our hardware.

The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents. While AV development today involves verification, validation, and testing, end-to-end assessment of AV systems under accidental faults in realistic driving scenarios has been largely unexplored. This paper presents DriveFI, a machine learning-based fault injection engine, which can mine situations and faults that maximally impact AV safety, as demonstrated on two industry-grade AV technology stacks (from NVIDIA and Baidu).

Deep neural networks have been adopted in a wide range of application domains, leading to high demand for inference accelerators. However, the high cost associated with ASIC hardware design makes it challenging to build custom accelerators for different targets. To lower design cost, we propose MAGNet, a modular accelerator generator for neural networks.

When describing images with natural language, descriptions can be made more informative if tuned for downstream tasks. This can be achieved by training two networks: a “speaker” that generates sentences given an image and a “listener” that uses them to perform a task. Unfortunately, training multiple networks jointly to communicate, faces two major challenges. First, the descriptions generated by a speaker network are discrete and stochastic, making optimization very hard and inefficient.

We describe an all-digital synchronizer that moves multi-bit signals between two periodic clock domains with an average delay of slightly more than a half cycle and an arbitrarily small probability of synchronization failure. The synchronizer operates by measuring the relative frequency of the two periodic clocks and using this frequency measurement, along with a phase detection, to compute a phase estimate. Interval arithmetic is used for the phase estimate to account for uncertainty.

Multi-view Stereo (MVS) is a common solution in photogrammetry applications for the dense reconstruction of a static scene from images. The static scene assumption, however, limits the general applicability of MVS algorithms, as many day-to-day scenes undergo non-rigid motion, e.g., clothes, faces, or human bodies. In this paper, we open up a new challenging direction: Dense 3D reconstruction of scenes with non-rigid changes observed from a small number of images sparsely captured from different views with a single monocular camera, which we call non-rigid multi-view stereo (NRMVS) problem.