Eric Brachmann
Axel Barroso-Laguna, Tommaso Cavallari, Victor Adrian Prisacariu,
TL;DR: FastForward - Efficient visual relocalization without building structured 3D maps. Relative pose between query and a set of retrieved mapping images.
Eric Brachmann, Jamie Wynn, Shuai Chen, Tommaso Cavallari, Áron Monszpart, Daniyar Turmukhambetov, Victor Adrian Prisacariu
TL;DR: self-supervised ACE = learning-based structure-from-motion, needs no pose priors, works on unordered image sets, efficiently handles thousands of images.
Axel Barroso-Laguna, Sowmya Munukutla, Victor Adrian Prisacariu, Eric Brachmann
TL;DR: MicKey, a method that regresses and matches scale-metric 3D key points, trained end-to-end using differentiable RANSAC
Eduardo Arnold, Jamie Wynn, Sara Vicente, Guillermo Garcia-Hernando, Aron Monszpart, Victor Prisacariu, Daniyar Turmukhambetov, Eric Brachmann
TL;DR: only one mapping image and one query, dataset with multiple hundred outdoor scenes, benchmark and online leaderboard
Aritra Bhowmik, Stefan Gumhold, Carsten Rother, Eric Brachmann
TL;DR: refine SuperPoint end-to-end for relative pose estimation, gradients of feature matching wrt feature descriptors and key point heatmap
Eric Brachmann, Carsten Rother
TL;DR: NG-RANSAC + NG-DSAC, gradients of RANSAC-fitted model wrt quality of data points, applied to E/F matrix fitting, horizon line estimation and camera relocalization
arXiv project page F/E matrix code horizon line code relocalisation code video
Tomas Hodan, Frank Michel, Eric Brachmann, Wadim Kehl, Anders Buch, Dirk Kraft, Bertram Drost, Joel Vidal, Stephan Ihrke , Xenophon Zabulis, Caner Sahin, Fabian Manhardt, Federico Tombari, Tae-Kyun Kim, Jiri Matas, Carsten Rother
TL;DR: de facto standard benchmark for instance pose estimation, unifying dataset formats and proposing evaluation metrics, ongoing competition with online leaderboard
Eric Brachmann, Carsten Rother
TL;DR: DSAC++, first time training scene coordinate regression without depth, differentiable PnP
Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother
TL;DR: gradients of a RANSAC-fitted model wrt the coordinates of the input points, using policy gradient on discrete hypothesis selection
Eric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, Carsten Rother
TL;DR: introduces dense image-to-object correspondences as a learnable intermediate representation, introduced the LINEMOD-Occlusion dataset
Leonard Bruns, Axel Barroso-Laguna, Tommaso Cavallari, Áron Monszpart, Sowmya Munukutla, Victor Adrian Prisacariu, Eric Brachmann
TL;DR: disentangle coordinate regression and latent map representation, pre-train the regressor on thousands of scenes to generalize from mapping data to difficult query images.
Wenjing Bian, Axel Barroso-Laguna, Tommaso Cavallari, Victor Adrian Prisacariu, Eric Brachmann
TL;DR: Combine ACE and ACE0 with various priors to stabilize reconstruction: leveraging RGB-D data if available, regularizing the scene-level depth distribution, utilize a 3D generative model trained on successful reconstructions.
Axel Barroso-Laguna, Tommaso Cavallari, Victor Adrian Prisacariu, Eric Brachmann
TL;DR: FastForward - Efficient visual relocalization without building structured 3D maps. Relative pose between query and a set of retrieved mapping images.
Van Nguyen Nguyen, Stephen Tyree, Andrew Guo, Mederic Fourmy, Anas Gouda, Taeyeop Lee, Sungphill Moon, Hyeontae Son, Lukas Ranftl, Jonathan Tremblay, Eric Brachmann, Bertram Drost, Vincent Lepetit, Carsten Rother, Stan Birchfield, Jiri Matas, Yann Labbe, Martin Sundermeyer, Tomas Hodan
TL;DR: results of BOP challenge 2024, significant progress for model-based pose localization of unseen objects, community has not yet signed on to the new task of model-free pose detection.
Eric Brachmann, Jamie Wynn, Shuai Chen, Tommaso Cavallari, Áron Monszpart, Daniyar Turmukhambetov, Victor Adrian Prisacariu
TL;DR: self-supervised ACE = learning-based structure-from-motion, needs no pose priors, works on unordered image sets, efficiently handles thousands of images.
Shuai Chen, Tommaso Cavallari, Victor Adrian Prisacariu, Eric Brachmann
TL;DR: marepo, a scene-agnostic absolute pose regression transformer on top of a scene-specific ACE map representation, on-par with structure-based relocalizers in terms of accuracy and mapping time
Axel Barroso-Laguna, Sowmya Munukutla, Victor Adrian Prisacariu, Eric Brachmann
TL;DR: MicKey, a method that regresses and matches scale-metric 3D key points, trained end-to-end using differentiable RANSAC
Tomas Hodan, Martin Sundermeyer, Yann Labbe, Van Nguyen Nguyen, Gu Wang, Eric Brachmann, Bertram Drost, Vincent Lepetit, Carsten Rother, Jiri Matas
TL;DR: results of BOP challenge 2023, accuracy is excellent if objects are known in advance, for unseen objects, still good but slow
Martin Sundermeyer, Tomas Hodan, Yann Labbe, Gu Wang, Eric Brachmann, Bertram Drost, Carsten Rother, Jiri Matas
TL;DR: results of BOP challenge 2022, deep neural networks beat everything else
Eduardo Arnold, Jamie Wynn, Sara Vicente, Guillermo Garcia-Hernando, Aron Monszpart, Victor Prisacariu, Daniyar Turmukhambetov, Eric Brachmann
TL;DR: only one mapping image and one query, dataset with multiple hundred outdoor scenes, benchmark and online leaderboard
Karren Yang, Michael Firman, Eric Brachmann, Clement Godard
TL;DR: camera pose by echolocation, relative pose / absolute pose / image retrieval, vision is more accurate but sound helps when vision fails
Tomas Hodan, Martin Sundermeyer, Bertram Drost, Yann Labbe, Eric Brachmann, Frank Michel, Carsten Rother, Jiri Matas
TL;DR: results of BOP challenge 2020, deep neural networks on par with point pair features
Aritra Bhowmik, Stefan Gumhold, Carsten Rother, Eric Brachmann
TL;DR: refine SuperPoint end-to-end for relative pose estimation, gradients of feature matching wrt feature descriptors and key point heatmap
Eric Brachmann, Carsten Rother
TL;DR: ESAC, end-to-end learning of mixture-of-experts and RANSAC, large scale scene coordinate regression
Eric Brachmann, Carsten Rother
TL;DR: NG-RANSAC + NG-DSAC, gradients of RANSAC-fitted model wrt quality of data points, applied to E/F matrix fitting, horizon line estimation and camera relocalization
arXiv project page F/E matrix code horizon line code relocalisation code video
Omid Hosseini Jafari, Siva Karthik Mustikovela, Karl Pertsch, Eric Brachmann, Carsten Rother
TL;DR: instance segmentation + deep object coordinate prediction
Tomas Hodan, Frank Michel, Eric Brachmann, Wadim Kehl, Anders Buch, Dirk Kraft, Bertram Drost, Joel Vidal, Stephan Ihrke , Xenophon Zabulis, Caner Sahin, Fabian Manhardt, Federico Tombari, Tae-Kyun Kim, Jiri Matas, Carsten Rother
TL;DR: de facto standard benchmark for instance pose estimation, unifying dataset formats and proposing evaluation metrics, ongoing competition with online leaderboard
Eric Brachmann
TL;DR: summary of my work prior to 2018, learning object and scene coordinate regression using random forests and neural networks
Eric Brachmann, Carsten Rother
TL;DR: DSAC++, first time training scene coordinate regression without depth, differentiable PnP
Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother
TL;DR: gradients of a RANSAC-fitted model wrt the coordinates of the input points, using policy gradient on discrete hypothesis selection
Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother
TL;DR: find pose inlier correspondences by optimizing the energy in a graphical model
Alexander Krull, Eric Brachmann, Sebastian Nowozin, Frank Michel, Jamie Shotton, Carsten Rother
TL;DR: an RL agent chooses which RANSAC hypothesis to refine next
Daniela Massiceti, Alexander Krull, Eric Brachmann, Carsten Rother, Philip H.S. Torr
TL;DR: mapping of random forests to NNs for optimization, and back again for efficiency
Eric Brachmann, Frank Michel, Alexander Krull, Michael Ying Yang, Stefan Gumhold, Carsten Rother
TL;DR: first object/scene coordinate regression system for RGB, predict correspondence distributions and search for max likelihood pose
Alexander Krull, Eric Brachmann, Frank Michel, Michael Ying Yang, Stefan Gumhold, Carsten Rother
TL;DR: substitute inlier counting pose score with a CNN that compares input image and renderings, trained via max likelihood
Frank Michel, Alexander Krull, Eric Brachmann, Michael Ying Yang, Stefan Gumhold, Carsten Rother
TL;DR: only n+2 correspondences are needed to estimate pose of n-jointed objects
Alexander Krull, Frank Michel, Eric Brachmann, Stefan Gumhold, Stephan Ihrke, Carsten Rother
TL;DR: combines RANSAC-based hypothesis sampling with particle filter for real-time pose tracking
Eric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, Carsten Rother
TL;DR: introduces dense image-to-object correspondences as a learnable intermediate representation, introduced the LINEMOD-Occlusion dataset
Eric Brachmann, Marcel Spehr, Stefan Gumhold
TL;DR: propagate visual words along image web edges to make a BoW image descriptors more robust
Eric Brachmann, Gero Dittmann, Klaus-Dieter Schubert
TL;DR: an authentication scheme for company intranets where you may want to trade security for simplicity
Feel free to use this website as template. Inspired by Jon Barron's iconic template.