Fetching the paper…
Reading the bibliography…
We propose a novel scene representation that encodes reaching distance -- the distance between any position in the scene to a goal along a feasible trajectory.
A note on two problems in connexion with graphs
Edsger W Dijkstra · 1959
Earlier work this paper cites.
Marching cubes: A high resolution 3d surface construction algorithm
William E. Lorensen and Harvey E. Cline · 1987
Earlier work this paper cites.
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Lydia E. Kavraki, Petr Svestka, J.-C. Latombe, and Mark H. Overmars · 1996
Earlier work this paper cites.
A fast marching level set method for monotonically advancing fronts
James A Sethian · 1996
Earlier work this paper cites.
Analysis of probabilistic roadmaps for path planning
Lydia E Kavraki, Mihail N Kolountzakis, and J-C Latombe · 1998
Earlier work this paper cites.
Rapidly-exploring random trees: A new tool for path planning
Steven M. LaValle · 1998
Earlier work this paper cites.
Rapidly-exploring random trees: Progress and prospects
Steven M. LaValle, James J. Kuffner, BR. Donald, et al · 2001
Earlier work this paper cites.
Rapidly-exploring random belief trees for motion planning under uncertainty
Adam Bry and Nicholas Roy · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Learning human activities and object affordances from rgb-d videos
Hema Swetha Koppula, Rudhir Gupta, and Ashutosh Saxena · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Physically grounded spatio-temporal object affordances
Hema S Koppula and Ashutosh Saxena · 2014
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Earlier work this paper cites.
Fast marching methods in path planning
Alberto Valero-Gomez, Javier Gómez, Santiago Garrido, and Luis Moreno · 2015
Earlier work this paper cites.
David Ha, Andrew Dai, and Quoc V Le · 2016
Earlier work this paper cites.
Value iteration networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel · 2016
Earlier work this paper cites.
Reasoning about object affordances in a knowledge base representation
Yuke Zhu, Alireza Fathi, and Li Fei-Fei · 2016
Earlier work this paper cites.
Fully convolutional instance-aware semantic segmentation
Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, and Yichen Wei · 2017
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Cited alongside, same era.
Learning from synthetic humans
Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev, and Cordelia Schmid · 2017
Cited alongside, same era.
Binge watching: Scaling affordance learning from sitcoms
Xiaolong Wang, Rohit Girdhar, and Abhinav Gupta · 2017
Cited alongside, same era.
On the complexity of exploration in goal-driven navigation
Maruan Al-Shedivat, Lisa Lee, Ruslan Salakhutdinov, and Eric Xing · 2018
Cited alongside, same era.
Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
Cited alongside, same era.
Learning to act properly: Predicting and explaining affordances from images
Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
Later among the works it cites.
DISN: Deep implicit surface network for high-quality single-view 3d reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 2019
Later among the works it cites.
Does computer vision matter for action?
Brady Zhou, Philipp Krähenbühl, and Vladlen Koltun · 2019
Later among the works it cites.
SAL: Sign agnostic learning of shapes from raw data
Matan Atzmon and Yaron Lipman · 2020
Later among the works it cites.
Learning with amigo: Adversarially motivated intrinsic goals
Andres Campero, Roberta Raileanu, Heinrich Küttler, Joshua B Tenenbaum, Tim Rocktäschel, and Edward Grefenstette · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ching-Yao Chuang, Jiaman Li, Antonio Torralba, and Sanja Fidler · 2018
Cited alongside, same era.
Alexander Sax, Bradley Emi, Amir R Zamir, Leonidas Guibas, Silvio Savarese, and Jitendra Malik · 2018
Cited alongside, same era.
Cyclical annealing schedule: A simple approach to mitigating KL vanishing
Hao Fu, Chunyuan Li, Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, and Lawrence Carin · 2019
Cited alongside, same era.
Digging into self-supervised monocular depth prediction
Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow · 2019
Cited alongside, same era.
Resolving 3d human pose ambiguities with 3d scene constraints
Mohamed Hassan, Vasileios Choutas, Dimitrios Tzionas, and Michael J. Black · 2019
Cited alongside, same era.
Putting humans in a scene: Learning affordance in 3d indoor environments
Xueting Li, Sifei Liu, Kihwan Kim, Xiaolong Wang, Ming-Hsuan Yang, and Jan Kautz · 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.
Long-term human motion prediction with scene context
Zhe Cao, Hang Gao, Karttikeya Mangalam, Qizhi Cai, Minh Vo, and Jitendra Malik · 2020
Later among the works it cites.
Bryan Chen, Alexander Sax, Gene Lewis, Iro Armeni, Silvio Savarese, Amir Zamir, Jitendra Malik, and Lerrel Pinto · 2020
Later among the works it cites.
Learned motion matching
Daniel Holden, Oussama Kanoun, Maksym Perepichka, and Tiberiu Popa · 2020
Later among the works it cites.
Local implicit grid representations for 3d scenes
Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, and Thomas Funkhouser · 2020
Later among the works it cites.
Grasping field: Learning implicit representations for human grasps
Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael Black, Krikamol Muandet, and Siyu Tang · 2020
Later among the works it cites.
Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
Later among the works it cites.
Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization
Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo · 2020
Later among the works it cites.
Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien N.P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein · 2020
Later among the works it cites.
Implicit mesh reconstruction from unannotated image collections
Shubham Tulsiani, Nilesh Kulkarni, and Abhinav Gupta · 2020
Later among the works it cites.
Learning to see before learning to act: Visual pre-training for manipulation
Lin Yen-Chen, Andy Zeng, Shuran Song, Phillip Isola, and Tsung-Yi Lin · 2020
Later among the works it cites.
Differentiable spatial planning using transformers
Devendra Singh Chaplot, Deepak Pathak, and Jitendra Malik · 2021
Closest in time.
Stochastic scene-aware motion prediction
Mohamed Hassan, Duygu Ceylan, Ruben Villegas, Jun Saito, Jimei Yang, Yi Zhou, and Michael Black · 2021
Closest in time.