Fetching the paper…
Reading the bibliography…
We present iNeRF, a framework that performs mesh-free pose estimation by "inverting" a Neural RadianceField (NeRF).
Über visuell wahrgenommene bewegungsrichtung
Hans Wallach · 1935
Earlier work this paper cites.
Ray tracing volume densities
James T. Kajiya and Brian P. Von Herzen · 1984
Earlier work this paper cites.
Optical models for direct volume rendering
Nelson Max · 1995
Earlier work this paper cites.
Simultaneous object recognition and segmentation from single or multiple model views
Vittorio Ferrari, Tinne Tuytelaars, and Luc Van Gool · 2006
Earlier work this paper cites.
3d object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints
Fred Rothganger, Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce · 2006
Earlier work this paper cites.
Image alignment and stitching: A tutorial
Richard Szeliski · 2006
Earlier work this paper cites.
The moped framework: Object recognition and pose estimation for manipulation
Alvaro Collet, Manuel Martinez, and Siddhartha S Srinivasa · 2011
Earlier work this paper cites.
Scene coordinate regression forests for camera relocalization in rgb-d images
Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon · 2013
Earlier work this paper cites.
Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models
Mathieu Aubry, Daniel Maturana, Alexei A Efros, Bryan C Russell, and Josef Sivic · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Rgb-d object recognition and pose estimation based on pre-trained convolutional neural network features
Max Schwarz, Hannes Schulz, and Sven Behnke · 2015
Earlier work this paper cites.
Viewpoints and keypoints
Shubham Tulsiani and Jitendra Malik · 2015
Earlier work this paper cites.
Exploiting uncertainty in regression forests for accurate camera relocalization
Julien Valentin, Matthias Nießner, Jamie Shotton, Andrew Fitzgibbon, Shahram Izadi, and Philip HS Torr · 2015
Earlier work this paper cites.
Self-supervised visual descriptor learning for dense correspondence
Tanner Schmidt, Richard Newcombe, and Dieter Fox · 2016
Earlier work this paper cites.
Modern Robotics
Kevin M Lynch and Frank C Park · 2017
Earlier work this paper cites.
6-DOF object pose from semantic keypoints
Georgios Pavlakos, Xiaowei Zhou, Aaron Chan, Konstantinos G Derpanis, and Kostas Daniilidis · 2017
Earlier work this paper cites.
Bop: Benchmark for 6d object pose estimation
Tomas Hodan, Frank Michel, Eric Brachmann, Wadim Kehl, Anders GlentBuch, Dirk Kraft, Bertram Drost, Joel Vidal, Stephan Ihrke, Xenophon Zabulis, et al · 2018
Cited alongside, same era.
Label fusion: A pipeline for generating ground truth labels for real rgbd data of cluttered scenes
Pat Marion, Peter R Florence, Lucas Manuelli, and Russ Tedrake · 2018
Cited alongside, same era.
End-to-end 6-DOF object pose estimation through differentiable rasterization
Andrea Palazzi, Luca Bergamini, Simone Calderara, and Rita Cucchiara · 2018
Cited alongside, same era.
Discovery of latent 3d keypoints via end-to-end geometric reasoning
Supasorn Suwajanakorn, Noah Snavely, Jonathan J Tompson, and Mohammad Norouzi · 2018
Cited alongside, same era.
Real-time seamless single shot 6D object pose prediction
Bugra Tekin, Sudipta N Sinha, and Pascal Fua · 2018
Cited alongside, same era.
Differentiable rendering: A survey
Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, and Adrien Gaidon · 2020
Closest in time.
Pointrend: Image segmentation as rendering
Alexander Kirillov, Yuxin Wu, Kaiming He, and Ross Girshick · 2020
Closest in time.
Sdf-srn: Learning signed distance 3d object reconstruction from static images
Chen-Hsuan Lin, Chaoyang Wang, and Simon Lucey · 2020
Closest in time.
Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
Closest in time.
Deep feedback inverse problem solver
Wei-Chiu Ma, Shenlong Wang, Jiayuan Gu, Sivabalan Manivasagam, Antonio Torralba, and Raquel Urtasun · 2020
Closest in time.
Nerf in the wild: Neural radiance fields for unconstrained photo collections
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jonathan Tremblay, Thang To, Balakumar Sundaralingam, Yu Xiang, Dieter Fox, and Stan Birchfield · 2018
Cited alongside, same era.
PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes
Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox · 2018
Cited alongside, same era.
Learning to predict 3d objects with an interpolation-based differentiable renderer
Wenzheng Chen, Huan Ling, Jun Gao, Edward Smith, Jaakko Lehtinen, Alec Jacobson, and Sanja Fidler · 2019
Cited alongside, same era.
Photometric mesh optimization for video-aligned 3d object reconstruction
Chen-Hsuan Lin, Oliver Wang, Bryan C Russell, Eli Shechtman, Vladimir G Kim, Matthew Fisher, and Simon Lucey · 2019
Cited alongside, same era.
kPAM: Keypoint affordances for category-level robotic manipulation
Lucas Manuelli, Wei Gao, Peter Florence, and Russ Tedrake · 2019
Cited alongside, same era.
Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Cited alongside, same era.
Local light field fusion: Practical view synthesis with prescriptive sampling guidelines
Ben Mildenhall, Pratul P Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar · 2019
Cited alongside, same era.
Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duckworth · 2020
Closest in time.
Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
Closest in time.
Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
Closest in time.
Latentfusion: End-to-end differentiable reconstruction and rendering for unseen object pose estimation
Keunhong Park, Arsalan Mousavian, Yu Xiang, and Dieter Fox · 2020
Closest in time.
Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
Closest in time.
GRF: Learning a general radiance field for 3D scene representation and rendering
Alex Trevithick and Bo Yang · 2020
Closest in time.
Self6d: Self-supervised monocular 6d object pose estimation
Gu Wang, Fabian Manhardt, Jianzhun Shao, Xiangyang Ji, Nassir Navab, and Federico Tombari · 2020
Closest in time.
ShaRF: Shape-conditioned radiance fields from a single view
Konstantinos Rematas, Ricardo Martin-Brualla, and Vittorio Ferrari · 2021
Closest in time.
Ibrnet: Learning multi-view image-based rendering
Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser · 2021
Closest in time.
pixelnerf: Neural radiance fields from one or few images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2021
Closest in time.