In this paper we introduce a visualization methodology to aid a human analyst in classifying adversity modes that impact lidar scan matching. Our methodology is intended for offline rather than real-time analysis. The method generates a vector-field plot that characterizes local discrepancies between a pair of registered point clouds. The vector field plot reveals patterns that would be difficult for the analyst to extract from raw point-cloud data. After introducing our methodology, we apply the process to two proof-of-concept examples: one a simulation study and the other a field experiment. For both data sets, a human analyst was able to reason about a series of adversity mechanisms and iteratively remove those mechanisms from the raw data, to help focus attention on progressively smaller discrepancies.

Stealing someone else's joke is one of the highest crimes in comedy. With new AI tools like ChatGPT, some comedians are now worried about getting ripped off. Comedian Matt Rife is facing backlash on social media after allegedly telling a 6-year-old boy that his mother buys his presents with profits from OnlyFans. In a Saturday video that has garnered over 13 million views, TikToker Bunny Hedaya claimed Rife had started "beef" with her child online. Hedaya's son drew Rife's attention after criticizing the comedian's recent Netflix standup special, "Natural Selection."

Real-time video frame interpolation (VFI) is very useful in video processing, media players, and display devices. We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI method, RIFE uses a neural network named IFNet that can estimate the intermediate flows end-to-end with much faster speed. A privileged distillation scheme is designed for stable IFNet training and improve the overall performance. RIFE does not rely on pre-trained optical flow models and can support arbitrary-timestep frame interpolation with the temporal encoding input. Experiments demonstrate that RIFE achieves state-of-the-art performance on several public benchmarks. Compared with the popular SuperSlomo and DAIN methods, RIFE is 4--27 times faster and produces better results. Furthermore, RIFE can be extended to wider applications thanks to temporal encoding. The code is available at https://github.com/megvii-research/ECCV2022-RIFE.

Real-Time Intermediate Flow Estimation for Video Frame Interpolation is the process of generating images from a sequence of frames. It is a challenging task as it requires a significant amount of computational resources. Moreover, rendering video can be a multi-step process. The quality of video interpolation is affected by many factors such as frame rate, quality of video encoding, the format of video content (e.g. The overall quality of the rendered video is highly dependent on the combination of all these factors.