RECORD
Future Segmentation Using 3D Structure
Computer Vision and Pattern Recognition · Submitted 28 Nov 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Ultimately, we observe that leveraging 3D structure in the model facilitates successful prediction, achieving state of the art accuracy in future semantic segmentation.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Predicting the future to anticipate the outcome of events and actions is a critical attribute of autonomous agents; particularly for agents which must rely heavily on real time visual data for decision making. Working towards this capability, we address the task of predicting future frame segmentation from a stream of monocular video by leveraging the 3D structure of the scene. Our framework is based on learnable sub-modules capable of predicting pixel-wise scene semantic labels, depth, and camera ego-motion of adjacent frames. We further propose a recurrent neural network based model capable of predicting future ego-motion trajectory as a function of a series of past ego-motion steps. Ultimately, we observe that leveraging 3D structure in the model facilitates successful prediction, achieving state of the art accuracy in future semantic segmentation.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Predicting the future to anticipate the outcome of events and actions is a critical attribute of autonomous agents; particularly for agents which must rely heavily on real time visual data for decision making. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s1 | Working towards this capability, we address the task of predicting future frame segmentation from a stream of monocular video by leveraging the 3D structure of the scene. | contribution 0.96 | 0.83 | 0.05 | asserted 0.83 |
| s2 | Our framework is based on learnable sub-modules capable of predicting pixel-wise scene semantic labels, depth, and camera ego-motion of adjacent frames. | method 0.98 | 0.84 | 0.04 | asserted 1.00 |
| s3 | We further propose a recurrent neural network based model capable of predicting future ego-motion trajectory as a function of a series of past ego-motion steps. | method 0.75 | 0.92 | 0.04 | asserted 0.98 |
| s4 | Ultimately, we observe that leveraging 3D structure in the model facilitates successful prediction, achieving state of the art accuracy in future semantic segmentation. | result 0.95 | 0.94 | 0.94 | asserted 0.99 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Predicting the future to anticipate the outcome of events and actions is a critical attribute of autonomous agents; particularly for agents which must rely heavily on real time visual data for decision making. Working towards this capability, we address the task of predicting future frame segmentation from a stream of monocular video by leveraging the 3D structure of the scene. Our framework is based on learnable sub-modules capable of predicting pixel-wise scene semantic labels, depth, and camera ego-motion of adjacent frames. We further propose a recurrent neural network based model capable of predicting future ego-motion trajectory as a function of a series of past ego-motion steps. Ultimately, we observe that leveraging 3D structure in the model facilitates successful prediction, achieving state of the art accuracy in future semantic segmentation.
Human review
How the literature uses it
4 arXiv papers cite this in 7 sentences
Papers citing it · 3 sentences without a confident label
Builds on it 1 paper
-
We accomplish this by extending the classic feature-to-feature (F2F) forecasting [ref] with a novel motion-to-feature (M2F) approach, where we introduce optical flow as another modality to guide the propagation of features and to enforce temporal consistency between the propagated features.
Long-term Video Frame Interpolation via Feature Propagation · Introduction · 0.98
Cites it as background 3 papers
-
Recent methods [ref] estimate future semantic segmentations by reasoning about shape, egomotion, and foreground motion separately.
Joint Forecasting of Panoptic Segmentations with Difference Attention · Related work · 0.96
-
Predicting the future environment was addressed by predicting future frames [ref] and future semantic segmentation [ref] .
Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior · Related Work · 0.86
-
This approach have been previously used for action recognition [ref] , instance segmentation [ref] and semantic segmentation [ref] tasks.
Long-term Video Frame Interpolation via Feature Propagation · Related Works · 0.86
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.