NeRF-: Neural Radiance Fields Without Known Camera Parameters

By Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, Victor Adrian Prisacariu. arXiv.org, 2021

712 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6caf3307096a15832ace34a0d54cd28413503f8b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction (2023, background): “…through the use of MLPs. Subsequent research has expanded the utility of NeRF to various applications, encompassing tasks such as mesh reconstruction from a collection of images [20, 45], inverse rendering [5, 25, 54], optimization of camera parameters [21, 47, 48], and few-shot learning [10, 51].”
NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images (2021, methodology): “This could potentially be addressed by jointly optimizing RawNeRF and the input camera poses [35, 50].”
Neural RGB-D Surface Reconstruction (2021, background): “Others have focused on resectioning a camera given a learned NeRF [85], and optimizing for the camera poses while learning a NeRF [41, 78].”
AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis (2021, methodology): “Follow-up works extend this idea by using in-the-wild training data including appearance interpolation [22], introducing deformable neural radiance fields to represent non-rigidly moving objects [24, 29], and optimizing NeRF without pre-computed camera parameters [41].”
Vision-Only Robot Navigation in a Neural Radiance World (2021, unknown): “Unlike voxel models, point clouds, or mesh models, they are trained directly on dense photographic images without needing precise feature extraction, matching, and alignment [4], [5].”
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM (2021, background): “Another set of works [21, 56, 65] tackle the problem of camera pose optimization, but they need a rather long optimization process, which is not suitable for real-time applications.”
BARF: Bundle-Adjusting Neural Radiance Fields (2021, background): “Concurrent to our work, NeRF--[60] introduced an empirical, two-stage pipeline to estimate unknown camera poses.”
KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs (2021, background): “A series of works improve generalization from limited number of training views [13,42,44,59,63,66,75] while others [57,68,73] remove the requirement for pose estimation.”
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review (2022, unknown): “BA and others NeRF– (2020) [73], BARF (2021) [74], SCNeRF (2021) [75]”
Nerfstudio: A Modular Framework for Neural Radiance Field Development (2023, methodology): “The Nerfacto method first optimizes camera views using an optimized SE(3) transformation [Lin et al. 2021; Tancik et al. 2022; Wang et al. 2021c].”
Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction (2021, background): “, relighting [2, 3, 50, 70], deformation [13, 38–40, 55], self-calibration [19, 27, 28, 35, 61], meta-learning [52], dynamic scene modeling [14, 25, 33, 41, 64], and generative modeling [5, 22, 44].”
SparseNeRF: Distilling Depth Ranking for Few-shot Novel View Synthesis (2023, background): “NeRF−− [42], GNeRF [24], and BARF [21] relaxed the requirements of NeRFs and synthesized novel views without perfect camera poses.”
NeuMan: Neural Human Radiance Field from a Single Video (2022, unknown): “While originally proposed to reconstruct a static scene with a set of posed images, it has since been quickly extended to dynamic scenes [28,15,29] and uncalibrated scenes [44,17].”
DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes (2023, unknown): “We also find that our baseline method, 3DGS [4], fails in modeling the detail areas, producing unpleasant artifacts, blurring, and unnatural colors.”
Fast Dynamic Radiance Fields with Time-Aware Neural Voxels (2022, background): “2021], camera parameter optimization [Lin et al. 2021; Wang et al. 2021b], rendering large-scale unbounded scenes [Zhang et al.”
Urban Radiance Fields (2021, background): “First, it assumes that good camera parameters are given through an SfM pipeline, but they are sometimes noisy in practice — a joint optimization of the camera parameters along with the network parameters may improve the reconstructions for those cases [37, 67].”
Extracting Triangular 3D Models, Materials, and Lighting From Images (2021, methodology): “NeRF [41] and followups [40, 43, 51, 63, 69], use volumetric representations and compute radiance by ray marching through a neurally encoded 5D light field.”