  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.
