Neural approaches have shown a significant progress on camera-based
reconstruction. But they require either a fairly dense sampling of the viewing
sphere, or pre-training on an existing dataset, thereby limiting their
generalizability. In contrast, photometric stereo (PS) approaches have shown
great potential for achieving high-quality reconstruction under sparse
viewpoints. Yet, they are impractical because they typically require tedious
laboratory conditions, are restricted to dark rooms, and often multi-staged,
making them subject to accumulated errors. To address these shortcomings, we
propose an end-to-end uncalibrated multi-view PS framework for reconstructing
high-resolution shapes acquired from sparse viewpoints in a real-world
environment. We relax the dark room assumption, and allow a combination of
static ambient lighting and dynamic near LED lighting, thereby enabling easy
data capture outside the lab. Experimental validation confirms that it
outperforms existing baseline approaches in the regime of sparse viewpoints by
a large margin. This allows to bring high-accuracy 3D reconstruction from the
dark room to the real world, while maintaining a reasonable data capture
complexity.

通过提出一种端到端的非标定多视点 PS 框架，以实现在真实环境中通过稀疏视点获取高分辨率形状的重建，摒弃了黑暗室的假设，并允许静态环境光和动态 LED 光的组合，从而实现了在实验室外轻松捕获数据，并通过实验证实，在稀疏视点情况下超越了现有的基准方法，将高精度三维重建从黑暗室引入到现实世界中，并保持了合理的数据采集复杂性。

稀疏视角，近光源：非标定点照明光度立体的实用范式

Sparse Views, Near Light: A Practical Paradigm for Uncalibrated  Point-light Photometric Stereo

Voice plays an important role in our lives by facilitating communication,
conveying emotions, and indicating health. Therefore, tracking vocal
interactions can provide valuable insight into many aspects of our lives. This
paper presents our ongoing efforts to design a new vocal tracking system we
call VoCopilot. VoCopilot is an end-to-end system centered around an
energy-efficient acoustic hardware and firmware combined with advanced machine
learning models. As a result, VoCopilot is able to continuously track
conversations, record them, transcribe them, and then extract useful insights
from them. By utilizing large language models, VoCopilot ensures the user can
extract useful insights from recorded interactions without having to learn
complex machine learning techniques. In order to protect the privacy of end
users, VoCopilot uses a novel wake-up mechanism that only records conversations
of end users. Additionally, all the rest of pipeline can be run on a commodity
computer (Mac Mini M2). In this work, we show the effectiveness of VoCopilot in
real-world environment for two use cases.

本文介绍了我们正在进行的设计一种名为 VoCopilot 的新型声音跟踪系统的努力，它是一个以节能的声学硬件和固件为中心，结合先进的机器学习模型的端到端系统。通过利用大型语言模型，VoCopilot 能够在不学习复杂的机器学习技术的情况下，持续跟踪对话，记录并转录它们，并从中提取有用的见解。为了保护终端用户的隐私，VoCopilot 使用一种新颖的唤醒机制，只记录终端用户的对话。此外，整个处理过程可以在一台普通的计算机上运行（Mac Mini M2）。在本工作中，我们展示了 VoCopilot 在两个使用场景的真实环境中的有效性。

VoCopilot：语音激活的日常互动追踪

VoCopilot: Voice-Activated Tracking of Everyday Interactions

Despite the intense attention and considerable investment into clinical
machine learning research, relatively few applications have been deployed at a
large-scale in a real-world clinical environment. While research is important
in advancing the state-of-the-art, translation is equally important in bringing
these techniques and technologies into a position to ultimately impact
healthcare. We believe a lack of appreciation for several considerations are a
major cause for this discrepancy between expectation and reality. To better
characterize a holistic perspective among researchers and practitioners, we
survey several practitioners with commercial experience in developing CML for
clinical deployment. Using these insights, we identify several main categories
of challenges in order to better design and develop clinical machine learning
applications.

通过调查拥有临床部署经验的从业者，本研究针对临床机器学习应用程序的主要挑战类别进行了分类，以更好地设计和开发临床机器学习应用程序。