Papers › TriStereoNet: A Trinocular Framework for Multi-baseline Disparity Estimation
TriStereoNet: A Trinocular Framework for Multi-baseline Disparity Estimation
Faranak Shamsafar, Andreas Zell
Stereo vision is an effective technique for depth estimation with broad applicability in autonomous urban and highway driving. While various deep learning-based approaches have been developed for stereo, the input data from a binocular setup with a fixed baseline are limited. Addressing such a problem, we present an end-to-end network for processing the data from a trinocular setup, which is a combination of a narrow and a wide stereo pair. In this design, two pairs of binocular data with a common reference image are treated with shared weights of the network and a mid-level fusion. We also propose a Guided Addition method for merging the 4D data of the two baselines. Additionally, an iterative sequential self-supervised and supervised learning on real and synthetic datasets is presented, making the training of the trinocular system practical with no need to ground-truth data of the real dataset. Experimental results demonstrate that the trinocular disparity network surpasses the scenario where individual pairs are fed into a similar architecture. Code and dataset: https://github.com/cogsys-tuebingen/tristereonet.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Stereo Depth Estimation | KITTI 2015 | TriStereoNet | D1-all All | 2.35 | #2 of 2 | Archive leaderboard | report |
| Stereo Depth Estimation | KITTI 2015 | TriStereoNet | D1-all Noc | 2.09 | #2 of 2 | Archive leaderboard | report |
| Stereo Depth Estimation | KITTI2015 | TriStereoNet | D1-all All | 2.35 | #6 of 7 | Archive leaderboard | report |
| Stereo Depth Estimation | KITTI2015 | TriStereoNet | D1-all Noc | 2.09 | #6 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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