DELTA: Dense Efficient Long-range 3D Tracking for any video

Determining optical flow (artificial intelligence laboratory)

BK Horn and B Schunck · 1980

Earlier work this paper cites.

Perceived size and motion in depth from optical expansion

Michael T Swanston and Walter C Gogel · 1986

Earlier work this paper cites.

Dense estimation and object-based segmentation of the optical flow with robust techniques

Etienne Mémin and Patrick Pérez · 1998

Earlier work this paper cites.

Perceiving visual expansion without optic flow

Paul R Schrater, David C Knill, and Eero P Simoncelli · 2001

Earlier work this paper cites.

High accuracy optical flow estimation based on a theory for warping

Thomas Brox, Andrés Bruhn, Nils Papenberg, and Joachim Weickert · 2004

Earlier work this paper cites.

Particle video: Long-range motion estimation using point trajectories

Peter Sand and Seth Teller · 2008

Earlier work this paper cites.

Kinecting the dots: Particle based scene flow from depth sensors

Simon Hadfield and Richard Bowden · 2011

Earlier work this paper cites.

A naturalistic open source movie for optical flow evaluation

Daniel J Butler, Jonas Wulff, Garrett B Stanley, and Michael J Black · 2012

Earlier work this paper cites.

A benchmark for the evaluation of rgb-d slam systems

Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers · 2012

Earlier work this paper cites.

Depth map prediction from a single image using a multi-scale deep network

David Eigen, Christian Puhrsch, and Rob Fergus · 2014

Earlier work this paper cites.

Sphereflow: 6 dof scene flow from rgb-d pairs

Michael Hornacek, Andrew Fitzgibbon, and Carsten Rother · 2014

Earlier work this paper cites.

Dense semi-rigid scene flow estimation from rgbd images

Julian Quiroga, Thomas Brox, Frédéric Devernay, and James Crowley · 2014

Earlier work this paper cites.

3d scene flow estimation with a piecewise rigid scene model

Christoph Vogel, Konrad Schindler, and Stefan Roth · 2015

Earlier work this paper cites.

Density estimation using real nvp

Original

Laurent Dinh, Jascha Narain Sohl-Dickstein, and Samy Bengio · 2016

Earlier work this paper cites.

Quo vadis, action recognition? a new model and the kinetics dataset

Joao Carreira and Andrew Zisserman · 2017

Earlier work this paper cites.

Flownet 2.0: Evolution of optical flow estimation with deep networks

Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017

Earlier work this paper cites.

Panoptic studio: A massively multiview system for social interaction

Hanbyul Joo, Tomas Simon, Xulong Li, Hao Liu, Lei Tan, Lin Gui, et al · 2017

Earlier work this paper cites.

The 2017 davis challenge on video object segmentation

Original

Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbeláez, Alex Sorkine-Hornung, and Luc Van Gool · 2017

Earlier work this paper cites.

Optical flow estimation using a spatial pyramid network

Anurag Ranjan and Michael J Black · 2017

Earlier work this paper cites.

Accurate optical flow via direct cost volume processing

Jia Xu, René Ranftl, and Vladlen Koltun · 2017

Earlier work this paper cites.

Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume

Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018

Earlier work this paper cites.

Hplflownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds

Xiuye Gu, Yijie Wang, Chongruo Wu, Yong Jae Lee, and Panqu Wang · 2019

Earlier work this paper cites.

Flownet3d: Learning scene flow in 3d point clouds

Xingyu Liu, Charles R Qi, and Leonidas J Guibas · 2019

Earlier work this paper cites.

Occupancy flow: 4d reconstruction by learning particle dynamics

Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2019

Cited alongside, same era.

Super-convergence: Very fast training of neural networks using large learning rates

Leslie N Smith and Nicholay Topin · 2019

Cited alongside, same era.

Web stereo video supervision for depth prediction from dynamic scenes

Chaoyang Wang, Simon Lucey, Federico Perazzi, and Oliver Wang · 2019

Cited alongside, same era.

End-to-end object detection with transformers

Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020

Cited alongside, same era.

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

Cited alongside, same era.

Raft: Recurrent all-pairs field transforms for optical flow

TAPIR: Tracking any point with per-frame initialization and temporal refinement

Carl Doersch, Yi Yang, Mel Vecerik, Dilara Gokay, Ankush Gupta, Yusuf Aytar, Joao Carreira, and Andrew Zisserman · 2023

Later among the works it cites.

Rethinking optical flow from geometric matching consistent perspective

Qiaole Dong, Chenjie Cao, and Yanwei Fu · 2023

Later among the works it cites.

Cotracker: It is better to track together

Original

Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, and Christian Rupprecht · 2023

Later among the works it cites.

Dinov2: Learning robust visual features without supervision, 2023

Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2023

Later among the works it cites.

Aria digital twin: A new benchmark dataset for egocentric 3d machine perception