25,522 research outputs found
Neural Radiance Fields: Past, Present, and Future
The various aspects like modeling and interpreting 3D environments and
surroundings have enticed humans to progress their research in 3D Computer
Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall
et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in
Computer Graphics, Robotics, Computer Vision, and the possible scope of
High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D
models have gained traction from res with more than 1000 preprints related to
NeRFs published. This paper serves as a bridge for people starting to study
these fields by building on the basics of Mathematics, Geometry, Computer
Vision, and Computer Graphics to the difficulties encountered in Implicit
Representations at the intersection of all these disciplines. This survey
provides the history of rendering, Implicit Learning, and NeRFs, the
progression of research on NeRFs, and the potential applications and
implications of NeRFs in today's world. In doing so, this survey categorizes
all the NeRF-related research in terms of the datasets used, objective
functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation
OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
Enhancing the robustness of vision algorithms in real-world scenarios is
challenging. One reason is that existing robustness benchmarks are limited, as
they either rely on synthetic data or ignore the effects of individual nuisance
factors. We introduce OOD-CV-v2, a benchmark dataset that includes
out-of-distribution examples of 10 object categories in terms of pose, shape,
texture, context and the weather conditions, and enables benchmarking of models
for image classification, object detection, and 3D pose estimation. In addition
to this novel dataset, we contribute extensive experiments using popular
baseline methods, which reveal that: 1) Some nuisance factors have a much
stronger negative effect on the performance compared to others, also depending
on the vision task. 2) Current approaches to enhance robustness have only
marginal effects, and can even reduce robustness. 3) We do not observe
significant differences between convolutional and transformer architectures. We
believe our dataset provides a rich test bed to study robustness and will help
push forward research in this area.
Our dataset can be accessed from https://bzhao.me/OOD-CV/Comment: arXiv admin note: substantial text overlap with arXiv:2111.1434
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