Slanted Stixels: Representing San Francisco's Steepest Streets
Open accessAbstract
In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Synthia is one of the oldest multimodal synthetic datasets providing labeled semantic segmentation samples.
“Synthia [70-72] is one of the oldest multimodal synthetic datasets providing labeled semantic segmentation samples.”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [71]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works
Self-citationThe Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works
Stereo Processing by Semiglobal Matching and Mutual Information
Hirschmüller, H. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1166 · Cited by 4,199 · see citing works
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.352 · Cited by 2,338 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · arXiv · doi:10.48550/arxiv.1604.01685 · Cited by 1,174 · see citing works
Self-citationPulling Things out of Perspective
Ladický, Ľ., Shi, J., & Pollefeys, M. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.19 · Cited by 463 · see citing works
Self-citationThe Stixel World - A Compact Medium Level Representation of the 3D-World
Badino, H., Franke, U., & Pfeiffer, D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03798-6_6 · Cited by 192 · see citing works
Self-citationEmbedded Real-time Stereo Estimation via Semi-global Matching on the GPU
Hernandez-Juarez, D., Chacón, A., Espinosa, A., Vázquez, D., Moure, J. C., & López, A. M. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.305 · Cited by 165 · see citing works
Self-citationStixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation
Levi, D., Garnett, N., & Fetaya, E. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.109 · Cited by 143 · see citing works
Towards a Global Optimal Multi-Layer Stixel Representation of Dense 3D Data
Pfeiffer, D., & Franke, U. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.51 · Cited by 95 · see citing works
Self-citationSemantic Stixels: Depth is not enough
Schneider, L., Cordts, M., Rehfeld, T., Pfeiffer, D., Enzweiler, M., Franke, U., Pollefeys, M., & Roth, S. (2016) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2016.7535373 · Cited by 90 · see citing works
Self-citationFusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter
Nuß, D., Yuan, T., Krehl, G., et al. (2015) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2015.7225827 · Cited by 66 · see citing works
Stixels estimation without depth map computation
Benenson, R., Timofte, R., & Gool, L. V. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130495 · Cited by 63 · see citing works
The Stixel World: A medium-level representation of traffic scenes
Cordts, M., Rehfeld, T., Schneider, L., Pfeiffer, D., Enzweiler, M., Roth, S., Pollefeys, M., & Franke, U. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.01.009 · Cited by 44 · see citing works
Self-citationKoch, R., Hornegger, J., & Jiang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11752-2 · Cited by 33 · see citing works
Object-Level Priors for Stixel Generation
Cordts, M., Schneider, L. M., Enzweiler, M., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11752-2_14 · Cited by 18 · see citing works
Self-citationGPU-Accelerated Real-Time Stixel Computation
Hernandez-Juarez, D., Espinosa, A., Moure, J. C., Vázquez, D., & López, A. M. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.122 · Cited by 17 · see citing works
Self-citationStix-Fusion: A Probabilistic Stixel Integration Technique
Muffert, M., Schneider, N., & Franke, U. (2014) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2014.11 · Cited by 9 · see citing works
Self-citationDhiman, V., Kundu, A., Dellaert, F., et al. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907129 · Cited by 8 · see citing works
Disparity image segmentation for free-space detection
Oana, I. (2016) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2016.7737150 · Cited by 6 · see citing works
Fast Obstacle Detection Using Sparse Edge-Based Disparity Maps
Carrillo, D. A. P., & Sutherland, A. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.80 · Cited by 4 · see citing works
Pattern recognition : 31st DAGM Symposium, Jena, Germany, September 9-11, 2009 : proceedings
Denzler, J., Notni, G., & Süße, H. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03798-6 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [21][21] David Pfeiffer, Stefan Gehrig, and Nicolai Schneider. Exploiting the power of stereo confidences. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013 , pages 297-304, 2013. doi: 10.1109/CVPR. 2013.45. URL http://dx.doi.org/10.1109/CVPR.2013.45 . - · DOI
Cited by
Sorted by citations
Multimodal end-to-end autonomous driving
Xiao, Y., Codevilla, F., Gurram, A., Urfalioglu, O., & Lopez, A. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3013234 · Cited by 319 · see citing works
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
- Authors
- Hernandez-Juarez, D., Schneider, L., Espinosa, A., Moure, J., Vazquez, D., López, A., Franke, U., & Pollefeys, M.
- ORCID
- Venue
- Procedings of the British Machine Vision Conference 2017
- Publisher
- British Machine Vision Association
- Published
- 2017
- OpenAlex
- W2736465832
- Cited by
- 53
- References
- 24
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 5 · 2022: 4 · 2021: 12 · 2020: 10 · 2019: 10 · 2018: 5
- FWCI
- 7.59
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- British Machine Vision Conference (BMVC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia (2014-SGR-1506)
- Centres de Recerca de Catalunya
- Generalitat de Catalunya (2014-SGR-1506)
- Ministerio de Economía y Competitividad (TIN2014-53234-C2-1-R)
- Ministerio de Ciencia e Innovación (TIN2014-53234-C2-1-R)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- stixels, scene representation, depth model, semantic segmentation, global energy minimization, over-segmentation, real-time inference, urban street scene
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
