Results 11 to 20 of about 10,601 (230)
SatGeo-NeRF: Geometrically Regularized NeRF for Satellite Imagery [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesWe present SatGeo-NeRF, a geometrically regularized NeRF for satellite imagery that mitigates overfitting-induced geometric artifacts observed in current state-of-the-art models using three model-agnostic regularizers.
V. Wagner +3 more
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NeRF-RPN: A general framework for object detection in NeRFs [PDF]
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF. Given a pre-trained NeRF model, NeRF-RPN aims to detect all bounding boxes of objects in a scene. By exploiting a novel voxel representation that incorporates multi-scale 3D neural volumetric features, we demonstrate it is possible to ...
Benran Hu 0001 +4 more
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VM-NeRF: Tackling Sparsity in NeRF with View Morphing [PDF]
, 2023 NeRF aims to learn a continuous neural scene representation by using a finite set of input images taken from various viewpoints. A well-known limitation of NeRF methods is their reliance on data: the fewer the viewpoints, the higher the likelihood of overfitting.
Matteo Bortolon +2 more
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HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork [PDF]
Advances in Neural Information Processing Systems 36, 2023 Project Page: https://hyp-nerf.github ...
Bipasha Sen +5 more
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Specularity in NeRFs: A Comparative Study of Ref-NeRF and NRFF
Image Processing On LineNeural Radiance Fields (NeRF) have emerged as a leading technology for 3D digitization, especially for their high accuracy and intricate detailing. Despite their advancements, early NeRF models struggle to handle reflections on specular surfaces effectively.
Albert Barreiro +5 more
openaire +2 more sources
Recursive-NeRF: An Efficient and Dynamically Growing NeRF
IEEE Transactions on Visualization and Computer Graphics, 2023 View synthesis methods using implicit continuous shape representations learned from a set of images, such as the Neural Radiance Field (NeRF) method, have gained increasing attention due to their high quality imagery and scalability to high resolution. However, the heavy computation required by its volumetric approach prevents NeRF from being useful in
Guo-Wei Yang +5 more
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Continuous Dynamic-NeRF: Spline-NeRF
CoRR, 2022 The problem of reconstructing continuous functions over time is important for problems such as reconstructing moving scenes, and interpolating between time steps. Previous approaches that use deep-learning rely on regularization to ensure that reconstructions are approximately continuous, which works well on short sequences.
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, 2023 First release of list of a curative list of Implicit Representations and NeRF papers relating to Robotics/RL ...
Muhammad Zubair Irshad
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CoRR, 2023 Neural Radiance Fields (NeRFs) learn to represent a 3D scene from just a set of registered images. Increasing sizes of a scene demands more complex functions, typically represented by neural networks, to capture all details. Training and inference then involves querying the neural network millions of times per image, which becomes impractically slow ...
Tim Elsner +5 more
openaire +3 more sources
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution [PDF]
, 2023 The neural radiance field (NeRF) achieved remarkable success in modeling 3D scenes and synthesizing high-fidelity novel views. However, existing NeRF-based methods focus more on the make full use of the image resolution to generate novel views, but less ...
Wang, Yuwang +4 more
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