We propose a novel rolling shutter bundle adjustment method for neural
radiance fields (NeRF), which utilizes the unordered rolling shutter (RS)
images to obtain the implicit 3D representation. Existing NeRF methods suffer
from low-quality images and inaccurate initial camera poses due to the RS
effect in the image, whereas, the previous method that incorporates the RS into
NeRF requires strict sequential data input, limiting its widespread
applicability. In constant, our method recovers the physical formation of RS
images by estimating camera poses and velocities, thereby removing the input
constraints on sequential data. Moreover, we adopt a coarse-to-fine training
strategy, in which the RS epipolar constraints of the pairwise frames in the
scene graph are used to detect the camera poses that fall into local minima.
The poses detected as outliers are corrected by the interpolation method with
neighboring poses. The experimental results validate the effectiveness of our
method over state-of-the-art works and demonstrate that the reconstruction of
3D representations is not constrained by the requirement of video sequence
input.

提出了一种新颖的滚动快门捆绑调整方法，用于神经辐射场（NeRF），利用无序滚动快门（RS）图像获取隐式 3D 表示。通过估计相机姿态和速度，恢复 RS 图像的物理形成，从而消除了对顺序数据的输入限制。采用自粗糙到精细的训练策略，利用场景图中成对帧的 RS 对极约束来检测落入局部极小值的相机姿态。通过与相邻姿态的插值方法对检测为异常值的姿态进行修正。实验结果验证了我们的方法在最新工作中的有效性，并证明 3D 表示的重建不受视频序列输入要求的限制。

URS-NeRF：面向神经辐射场的无序滚动快门束调整

URS-NeRF: Unordered Rolling Shutter Bundle Adjustment for Neural  Radiance Fields

We introduce Structured 3D Features, a model based on a novel implicit 3D
representation that pools pixel-aligned image features onto dense 3D points
sampled from a parametric, statistical human mesh surface. The 3D points have
associated semantics and can move freely in 3D space. This allows for optimal
coverage of the person of interest, beyond just the body shape, which in turn,
additionally helps modeling accessories, hair, and loose clothing. Owing to
this, we present a complete 3D transformer-based attention framework which,
given a single image of a person in an unconstrained pose, generates an
animatable 3D reconstruction with albedo and illumination decomposition, as a
result of a single end-to-end model, trained semi-supervised, and with no
additional postprocessing. We show that our S3F model surpasses the previous
state-of-the-art on various tasks, including monocular 3D reconstruction, as
well as albedo and shading estimation. Moreover, we show that the proposed
methodology allows novel view synthesis, relighting, and re-posing the
reconstruction, and can naturally be extended to handle multiple input images
(e.g. different views of a person, or the same view, in different poses, in
video). Finally, we demonstrate the editing capabilities of our model for 3D
virtual try-on applications.

本研究提出了一种基于隐式 3D 表示的 Structured 3D Features 模型，通过对参数统计人体网格表面进行密集的 3D 点采样并将像素对齐的图像特征汇集到其中，生成一个单端到端模型，可以生成带有颜色和光照分解的动画化 3D 重建图像，在 3D 虚拟试穿等应用领域有很大应用潜力。