
ROBUSTNERF . GITHUB . IO {
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Title:
RobustNeRF
Description:
Given a monocular video, segmenting and decoupling dynamic objects while recovering the static environment is a widely studied problem in machine intelligence. Existing solutions usually approach this problem in the image domain, limiting their performance and understanding of the environment. We introduce Decoupled Dynamic Neural Radiance Field (D^2NeRF), a self-supervised approach that takes a monocular video and learns a 3D scene representation which decouples moving objects, including their shadows, from the static background. Our method represents the moving objects and the static background by two separate neural radiance fields with only one allowing for temporal changes. A naive implementation of this approach leads to the dynamic component taking over the static one as the representation of the former is inherently more general and prone to overfitting. To this end, we propose a novel loss to promote correct separation of phenomena. We further propose a shadow field network to detect and decouple dynamically moving shadows. We introduce a new dataset containing various dynamic objects and shadows and demonstrate that our method can achieve better performance than state-of-the-art approaches in decoupling dynamic and static 3D objects, occlusion and shadow removal, and image segmentation for moving objects.
Website Age:
12 years and 4 months (reg. 2013-03-08).
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Keywords {🔍}
distractors, comparisons, robustnerfleft, mipnerf, dataset, robust, nerf, robustnerf, ignoring, losses, cvpr, paper, sara, sabour, suhani, vora, daniel, duckworth, ivan, krasin, david, fleet, andrea, tagliasacchi, university, data, scene, scenes, objects, training, modeling, outliers, optimization, problem, highlight, google, deepmind, toronto, simon, fraser, arxiv, video, slides, poster, code, teaser, abstract, neural, radiance, fields,
Topics {✒️}
real-world scenes ieee/cvf conference view-dependent effects scenes include distractors babyyoda distractors dataset crab distractors dataset statue distractors dataset android distractors dataset modern nerf frameworks modeling transient objects highlight paper robust losses} modeling distractors multi-view moving objects robust estimation ignoring distractors calibrated images image capture lighting variations nerf training optimization problem hyper-parameters priori knowledge pre-processing booktitle = {proceedings computer vision pattern recognition website template ref-nerf sara sabour1 suhani vora1 andrea tagliasacchi1 static scene training data results comparisons outliers daniel ivan distractors nerf cvpr data scene sara suhani andrea} comparisons robustnerf david
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