We introduce an improved solution to the neural image-based rendering problem
in computer vision. Given a set of images taken from a freely moving camera at
train time, the proposed approach could synthesize a realistic image of the
scene from a novel viewpoint at test time. The key ideas presented in this
paper are (i) Recovering accurate camera parameters via a robust pipeline from
unposed day-to-day images is equally crucial in neural novel view synthesis
problem; (ii) It is rather more practical to model object's content at
different resolutions since dramatic camera motion is highly likely in
day-to-day unposed images. To incorporate the key ideas, we leverage the
fundamentals of scene rigidity, multi-scale neural scene representation, and
single-image depth prediction. Concretely, the proposed approach makes the
camera parameters as learnable in a neural fields-based modeling framework. By
assuming per view depth prediction is given up to scale, we constrain the
relative pose between successive frames. From the relative poses, absolute
camera pose estimation is modeled via a graph-neural network-based multiple
motion averaging within the multi-scale neural-fields network, leading to a
single loss function. Optimizing the introduced loss function provides camera
intrinsic, extrinsic, and image rendering from unposed images. We demonstrate,
with examples, that for a unified framework to accurately model multiscale
neural scene representation from day-to-day acquired unposed multi-view images,
it is equally essential to have precise camera-pose estimates within the scene
representation framework. Without considering robustness measures in the camera
pose estimation pipeline, modeling for multi-scale aliasing artifacts can be
counterproductive. We present extensive experiments on several benchmark
datasets to demonstrate the suitability of our approach.

提出了一种改进的神经图像渲染问题的解决方案，通过从不同分辨率的图像中恢复准确的相机参数并利用多尺度神经场网络进行绝对相机姿态估计，该方法实现了从不同视角合成逼真的场景图像。

从非姿态图像中学习鲁棒的多尺度神经辐射场表示

Learning Robust Multi-Scale Representation for Neural Radiance Fields  from Unposed Images

We introduce a novel approach for monocular novel view synthesis of dynamic
scenes. Existing techniques already show impressive rendering quality but tend
to focus on optimization within a single scene without leveraging prior
knowledge. This limitation has been primarily attributed to the lack of
datasets of dynamic scenes available for training and the diversity of scene
dynamics. Our method FlowIBR circumvents these issues by integrating a neural
image-based rendering method, pre-trained on a large corpus of widely available
static scenes, with a per-scene optimized scene flow field. Utilizing this flow
field, we bend the camera rays to counteract the scene dynamics, thereby
presenting the dynamic scene as if it were static to the rendering network. The
proposed method reduces per-scene optimization time by an order of magnitude,
achieving comparable results to existing methods - all on a single
consumer-grade GPU.

我们引入了一种新颖的方法，用于动态场景的单目新视图合成。通过将场景动态性与预训练的神经图像渲染方法相结合，我们的方法绕过了训练数据和场景动态性多样性的限制，通过优化场景流场来实现将动态场景呈现为静态场景，从而降低了优化时间并实现了与现有方法相当的渲染质量。