This paper proposes a practical photometric solution for the challenging
problem of in-the-wild inverse rendering under unknown ambient lighting. Our
system recovers scene geometry and reflectance using only multi-view images
captured by a smartphone. The key idea is to exploit smartphone's built-in
flashlight as a minimally controlled light source, and decompose image
intensities into two photometric components -- a static appearance corresponds
to ambient flux, plus a dynamic reflection induced by the moving flashlight.
Our method does not require flash/non-flash images to be captured in pairs.
Building on the success of neural light fields, we use an off-the-shelf method
to capture the ambient reflections, while the flashlight component enables
physically accurate photometric constraints to decouple reflectance and
illumination. Compared to existing inverse rendering methods, our setup is
applicable to non-darkroom environments yet sidesteps the inherent difficulties
of explicit solving ambient reflections. We demonstrate by extensive
experiments that our method is easy to implement, casual to set up, and
consistently outperforms existing in-the-wild inverse rendering techniques.
Finally, our neural reconstruction can be easily exported to PBR textured
triangle mesh ready for industrial renderers.

本论文提出了一种针对野外未知环境光逆渲染的实用光度解决方案，利用智能手机内置闪光灯作为微控光源并将图像强度分解成两个光度分量，其方法无需成对捕获闪光 / 非闪光图像，能够解耦反射和照明，并实现物理精确的光度约束，具有易于实现、适用于非黑房环境且性能优于现有技术的特点。

野外反渲染：使用手电筒进行的自然环境下的反渲染

WildLight: In-the-wild Inverse Rendering with a Flashlight

Virtual Product placement(VPP) is the advertising technique of digitally
placing a branded object into the scene of a movie or TV show. This type of
advertising provides the ability for brands to reach consumers without
interrupting the viewing experience with a commercial break, as the products
are seen in the background or as props. Despite this being a billion-dollar
industry, ad rendering technique is currently executed at post production
stage, manually either with the help of VFx artists or through semi-automated
solutions. In this paper, we demonstrate a fully automated framework to
digitally place 2-D ads in linear TV cooking shows captured using single-view
camera with small camera movements. Without access to full video or production
camera configuration, this framework performs the following tasks (i)
identifying empty space for 2-D ad placement (ii) kitchen scene understanding
(iii) occlusion handling (iv) ambient lighting and (v) ad tracking.

该研究论文介绍了一种完全自动的框架，可在单视角摄录制的线性电视烹饪展示中，识别空白区域，进行框内广告放置，厨房场景理解、遮挡处理、环境光照和广告跟踪等多种大数据任务，实现了产品虚拟放置技术的自动化。

线性电视广告 2D 投放框架

Framework for 2D Ad placements in LinearTV

Single-photon avalanche diodes (SPADs) are an emerging technology with a
unique capability of capturing individual photons with high timing precision.
SPADs are being used in several active imaging systems (e.g., fluorescence
lifetime microscopy and LiDAR), albeit mostly limited to low photon flux
settings. We propose passive free-running SPAD (PF-SPAD) imaging, an imaging
modality that uses SPADs for capturing 2D intensity images with unprecedented
dynamic range under ambient lighting, without any active light source. Our key
observation is that the precise inter-photon timing measured by a SPAD can be
used for estimating scene brightness under ambient lighting conditions, even
for very bright scenes. We develop a theoretical model for PF-SPAD imaging, and
derive a scene brightness estimator based on the average time of darkness
between successive photons detected by a PF-SPAD pixel. Our key insight is that
due to the stochastic nature of photon arrivals, this estimator does not suffer
from a hard saturation limit. Coupled with high sensitivity at low flux, this
enables a PF-SPAD pixel to measure a wide range of scene brightness, from very
low to very high, thereby achieving extreme dynamic range. We demonstrate an
improvement of over 2 orders of magnitude over conventional sensors by imaging
scenes spanning a dynamic range of 1,000,000:1.

本研究提出了一种名为被动自由运行 SPAD 成像的技术，利用了单光子雪崩二极管的高时间精度，可以在自然光下捕捉 2D 图像。通过理论模型和场景亮度估计器，跨越了低到极高亮度的极宽动态范围，相对于传统传感器的场景动态范围提高了两个数量级。