This repository contains an implementation for the paper "Cas-FNE: Cascaded Face Normal Estimation". Although recent approaches to face normal estimation have achieved promising results, their effectiveness heavily depends on large-scale paired data for training.

2024年11月21日 · To overcome the above issue, we propose a simple yet effective cascaded neural network, called Cas-Fne, which progressively boosts the quality of predicted normals with marginal model parameters and computational cost.

FINE 和 FINE+RAW 使用较低 JPEG 压缩率以记录高质量 JPEG 图像，而 NORMAL 和 NORMAL+RAW 则使用较高 JPEG 压缩率以增加可保存的图像数量。 若要开启或关闭 RAW 图 …

2023年8月8日 · Flexible nasopharyngoscopy (also called fiberoptic nasendoscopy/flexible nasolaryngoscopy/flexible fiberoptic nasopharyngolaryngoscopy) is an essential skill for any otorhinolaryngologist (ENT surgeon). It is a diagnostic procedure used for examination of the nose, throat, and airway.

2024年12月1日 · Quantitative and qualitative evaluations on benchmark datasets are conducted to show that the proposed cascaded neural network, called Cas-FNE, can faithfully maintain facial details and reveal its superiority over state-of-the-art methods.

2022年10月10日 · In this paper, We present a coarse-to-fine framework to estimate face normal from an in-the-wild image with only a coarse exemplar reference. In this manner, our method can largely alleviate the negative impact caused by lacking training data, and focus on exploring the high-fidelity normal contained in natural images.

Capturing high-fidelity normals from single face images plays a core role in numerous computer vision and graphics applications. Though significant progress has been made in recent years, how to effectively and efficiently explore normal priors remains challenging. Most existing approaches depend on the development of intricate network ...

This repository contains the official implementation of "Towards High-fidelity Face Normal Estimation" Paper, which appears in ACM Multimedia 2022. For testing run: HFFNE is …

2022年1月1日 · In most studies, the FNE is mainly characterized by prolonged sleep onset latency (SOL), less total sleep time (TST) and rapid eye movement (REM) sleep, lower sleep efficiency (SE) and greater frequency of awakening during the initial PSG recording night in comparison with subsequent PSG recording nights [[2], [3], [4], [5], [6], [7]].

中文关键词： Cascaded learning face normal progressive refine-ment shared-parameter 英文关键词： Cascaded learning face normal progressive refine-ment shared-parameter 刊名：《自动化学报（英文版）》 2024 (12) 页码/页数：2423-2434,12