Fetching the paper…
Reading the bibliography…
This paper proposes a concise, elegant, and robust pipeline to estimate smooth camera trajectories and obtain dense point clouds for casual videos in the wild.
Representing moving images with layers
Wang, J. Y. and Adelson, E. H · 1994
Earlier work this paper cites.
Motion segmentation and tracking using normalized cuts
Shi, J. and Malik, J · 1998
Earlier work this paper cites.
Variational motion segmentation with level sets
Brox, T., Bruhn, A., and Weickert, J · 2006
Earlier work this paper cites.
Parallel tracking and mapping for small ar workspaces
Klein, G. and Murray, D · 2007
Earlier work this paper cites.
Consistent depth maps recovery from a video sequence
Zhang, G., Jia, J., Wong, T.-T., and Bao, H · 2009
Earlier work this paper cites.
Large displacement optical flow: descriptor matching in variational motion estimation
Brox, T. and Malik, J · 2010
Earlier work this paper cites.
Dtam: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., and Davison, A. J · 2011
Earlier work this paper cites.
A benchmark for the evaluation of rgb-d slam systems
Sturm, J., Engelhard, N., Endres, F., Burgard, W., and Cremers, D · 2012
Earlier work this paper cites.
Efficient and robust large-scale rotation averaging
Chatterjee, A. and Govindu, V. M · 2013
Earlier work this paper cites.
Robust monocular slam in dynamic environments
Tan, W., Liu, H., Dong, Z., Zhang, G., and Bao, H · 2013
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., and Fergus, R · 2014
Earlier work this paper cites.
Lsd-slam: Large-scale direct monocular slam
Engel, J., Schöps, T., and Cremers, D · 2014
Earlier work this paper cites.
Svo: Fast semi-direct monocular visual odometry
Forster, C., Pizzoli, M., and Scaramuzza, D · 2014
Earlier work this paper cites.
Global structure-from-motion by similarity averaging
Cui, Z. and Tan, P · 2015
Earlier work this paper cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
Eigen, D. and Fergus, R · 2015
Earlier work this paper cites.
Robust camera location estimation by convex programming
Ozyesil, O. and Singer, A · 2015
Earlier work this paper cites.
Theia: A fast and scalable structure-from-motion library
Sweeney, C., Hollerer, T., and Turk, M · 2015
Earlier work this paper cites.
Svo: Semidirect visual odometry for monocular and multicamera systems
Forster, C., Zhang, Z., Gassner, M., Werlberger, M., and Scaramuzza, D · 2016
Earlier work this paper cites.
A benchmark dataset and evaluation methodology for video object segmentation
Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., and Sorkine-Hornung, A · 2016
Earlier work this paper cites.
Structure-from-motion revisited
Schonberger, J. L. and Frahm, J.-M · 2016
Cited alongside, same era.
Structure-from-motion revisited
Schönberger, J. L. and Frahm, J.-M · 2016
Cited alongside, same era.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., and Nießner, M · 2017
Cited alongside, same era.
Direct sparse odometry
Engel, J., Koltun, V., and Cremers, D · 2017
Cited alongside, same era.
evo: Python package for the evaluation of odometry and slam
Grupp, M · 2017
Cited alongside, same era.
Mask r-cnn
He, K., Gkioxari, G., Dollar, P., and Girshick, R · 2017
Cited alongside, same era.
Flownet 2.0: Evolution of optical flow estimation with deep networks
Superglue: Learning feature matching with graph neural networks
Sarlin, P.-E., DeTone, D., Malisiewicz, T., and Rabinovich, A · 2020
Later among the works it cites.
Tartanvo: A generalizable learning-based vo
Wang, W., Hu, Y., and Scherer, S · 2020
Later among the works it cites.
D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry
Yang, N., Stumberg, L. v., Wang, R., and Cremers, D · 2020
Later among the works it cites.
Motion-attentive transition for zero-shot video object segmentation
Zhou, T., Wang, S., Zhou, Y., Yao, Y., Li, J., and Shao, L · 2020
Later among the works it cites.
Robust consistent video depth estimation
Kopf, J., Rong, X., and Huang, J.-B · 2021
Later among the works it cites.
Unsupervised Joint Learning of Depth, Optical Flow, Ego-motion from Video
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., and Brox, T · 2017
Cited alongside, same era.
Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras
Mur-Artal, R. and Tardós, J. D · 2017
Cited alongside, same era.
Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks
Wang, S., Clark, R., Wen, H., and Trigoni, N · 2017
Cited alongside, same era.
Dynaslam: Tracking, mapping, and inpainting in dynamic scenes
Bescos, B., Fácil, J. M., Civera, J., and Neira, J · 2018
Cited alongside, same era.
Superpoint: Self-supervised interest point detection and description
DeTone, D., Malisiewicz, T., and Rabinovich, A · 2018
Cited alongside, same era.
Megadepth: Learning single-view depth prediction from internet photos
Li, Z. and Snavely, N · 2018
Cited alongside, same era.
Li, J., Zhao, J., Song, S., and Feng, T · 2021
Later among the works it cites.
The emergence of objectness: Learning zero-shot segmentation from videos
Liu, R., Wu, Z., Yu, S., and Lin, S · 2021
Later among the works it cites.
DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
Teed, Z. and Deng, J · 2021
Later among the works it cites.
Self-supervised video object segmentation by motion grouping
Yang, C., Lamdouar, H., Lu, E., Zisserman, A., and Xie, W · 2021
Later among the works it cites.
Particle video revisited: Tracking through occlusions using point trajectories
Harley, A. W., Fang, Z., and Fragkiadaki, K · 2022
Later among the works it cites.
Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., and Koltun, V · 2022
Later among the works it cites.
Gmflow: Learning optical flow via global matching
Xu, H., Zhang, J., Cai, J., Rezatofighi, H., and Tao, D · 2022
Later among the works it cites.
Deflowslam: Self-supervised scene motion decomposition for dynamic dense slam
Ye, W., Lan, X., Chen, S., Ming, Y., Yu, X., Li, J., Bao, H., Cui, Z., and Zhang, G · 2022
Later among the works it cites.
Particlesfm: Exploiting dense point trajectories for localizing moving cameras in the wild
Zhao, W., Liu, S., Guo, H., Wang, W., and Liu, Y.-J · 2022
Later among the works it cites.
Midas v3.1 – a model zoo for robust monocular relative depth estimation
Birkl, R., Wofk, D., and Müller, M · 2023
Later among the works it cites.
Tapir: Tracking any point with per-frame initialization and temporal refinement
Doersch, C., Yang, Y., Vecerik, M., Gokay, D., Gupta, A., Aytar, Y., Carreira, J., and Zisserman, A · 2023
Later among the works it cites.
Oneformer: One transformer to rule universal image segmentation
Jain, J., Li, J., Chiu, M. T., Hassani, A., Orlov, N., and Shi, H · 2023
Later among the works it cites.
CoTracker: It is better to track together
Karaev, N., Rocco, I., Graham, B., Neverova, N., Vedaldi, A., and Rupprecht, C · 2023
Later among the works it cites.
PVO: Panoptic Visual Odometry
Ye, W., Lan, X., Chen, S., Ming, Y., Yu, X., Bao, H., Cui, Z., and Zhang, G · 2023
Later among the works it cites.