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NeRF++: Analyzing and Improving Neural Radiance Fields.
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TLDR
A parametrization issue involved in applying NeRF to 360 captures of objects within large-scale, unbounded 3D scenes is addressed, and the method improves view synthesis fidelity in this challenging scenario.Abstract:
Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes and forward-facing capture of bounded and unbounded scenes. NeRF fits multi-layer perceptrons (MLPs) representing view-invariant opacity and view-dependent color volumes to a set of training images, and samples novel views based on volume rendering techniques. In this technical report, we first remark on radiance fields and their potential ambiguities, namely the shape-radiance ambiguity, and analyze NeRF's success in avoiding such ambiguities. Second, we address a parametrization issue involved in applying NeRF to 360 captures of objects within large-scale, unbounded 3D scenes. Our method improves view synthesis fidelity in this challenging scenario. Code is available at this https URL.read more
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Proceedings ArticleDOI
TensoRF: Tensorial Radiance Fields
TL;DR: TensoRF is presented, a novel approach to model and reconstruct radiance fields as a 4D tensor, which represents a 3D voxel grid with per-voxel multi-channel features, and a novel vector-matrix (VM) decomposition that relaxes the low-rank constraints for two modes of a tensor and factorizes tensors into compact vector and matrix factors.
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NeRD: Neural Reflectance Decomposition from Image Collections
TL;DR: A neural reflectance decomposition (NeRD) technique that uses physically-based rendering to decompose the scene into spatially varying BRDF material properties enabling fast real-time rendering with novel illuminations.
Proceedings ArticleDOI
Block-NeRF: Scalable Large Scene Neural View Synthesis
Matthew Tancik,Vincent Casser,Xinchen Yan,Sabeek Pradhan,Ben Mildenhall,Pratul P. Srinivasan,Jonathan T. Barron,Henrik Kretzschmar +7 more
TL;DR: It is demonstrated that when scaling NeRF to render city-scale scenes spanning multiple blocks, it is vital to de-compose the scene into individually trained NeRFs, which decouples rendering time from scene size, enables rendering to scale to arbitrarily large environments, and allows per-block updates of the environment.
Proceedings ArticleDOI
NeX: Real-time View Synthesis with Neural Basis Expansion
TL;DR: NeX as discussed by the authors models view-dependent effects by parameterizing each pixel as a linear combination of basis functions learned from a neural network and proposes a hybrid implicit-explicit modeling strategy that improves upon fine detail.
Proceedings ArticleDOI
Advances in neural rendering
Ayush Tewari,Ohad Fried,Justus Thies,Vincent Sitzmann,Stephen Lombardi,Zexiang Xu,Tomas Simon,Matthias Nießner,Edgar Tretschk,Lingjie Liu,Ben Mildenhall,Pratul P. Srinivasan,Rohit Pandey,Sergio Orts-Escolano,Sean Fanello,M. Guo,Gordon Wetzstein,Jun-Yan Zhu,Christian Theobalt,Maneesh Agrawala,Dan B. Goldman,Michael Zollhöfer +21 more
TL;DR: Loss functions for Neural Rendering Jun-Yan Zhu shows the importance of knowing the number of neurons in the system and how many neurons are firing at the same time.
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