Level Set Stereo for Cooperative Grouping with Occlusion
- URL: http://arxiv.org/abs/2006.16094v3
- Date: Fri, 18 Jun 2021 05:16:35 GMT
- Title: Level Set Stereo for Cooperative Grouping with Occlusion
- Authors: Jialiang Wang and Todd Zickler
- Abstract summary: Localizing stereo boundaries is difficult because matching cues are absent in the occluded regions that are adjacent to them.
We introduce an energy and level-set disparity that improves boundaries by encoding the essential geometry of occlusions.
- Score: 5.837881923712393
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Localizing stereo boundaries is difficult because matching cues are absent in
the occluded regions that are adjacent to them. We introduce an energy and
level-set optimizer that improves boundaries by encoding the essential geometry
of occlusions: The spatial extent of an occlusion must equal the amplitude of
the disparity jump that causes it. In a collection of figure-ground scenes from
Middlebury and Falling Things stereo datasets, the model provides more accurate
boundaries than previous occlusion-handling techniques.
Related papers
- Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model [62.37493746544967]
Camera-based setups offer a cost-effective option by using stereo depth estimation to generate dense, high-resolution depth maps.
Existing omnidirectional stereo matching approaches achieve only limited depth accuracy across diverse environments.
We present DFI-OmniStereo, a novel omnidirectional stereo matching method that leverages a large-scale pre-trained foundation model for relative monocular depth estimation.
arXiv Detail & Related papers (2025-03-30T16:24:22Z) - Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions [58.88917836512819]
We propose a novel framework incorporating stereo depth estimation to enforce accurate geometric constraints.
To mitigate the effects of poor lighting on stereo matching, we introduce Degradation Masking.
Our method achieves state-of-the-art (SOTA) performance on the Multi-Spectral Stereo (MS2) dataset.
arXiv Detail & Related papers (2024-11-06T03:30:46Z) - Mixed-State Topological Order under Coherent Noises [2.8391355909797644]
We find remarkable stability of mixed-state topological order under random rotation noise with axes near the $Y$-axis of qubits.
The upper bounds for the intrinsic error threshold are determined by these phase boundaries, beyond which quantum error correction becomes impossible.
arXiv Detail & Related papers (2024-11-05T19:00:06Z) - Global Occlusion-Aware Transformer for Robust Stereo Matching [11.655465312241699]
This paper introduces a novel attention-based stereo-matching network called Global Occlusion-Aware Transformer (GOAT)
GOAT exploits long-range dependency and occlusion-awareness global context for disparity estimation.
The proposed GOAT demonstrates outstanding performance among all benchmarks, particularly in the occluded regions.
arXiv Detail & Related papers (2023-12-22T12:34:58Z) - EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video
Grounding with Multimodal Large Language Model [63.93372634950661]
We propose a new perspective that maintains the integrity of the original temporal content while introducing more valuable information for expanding the incomplete boundaries.
Motivated by video continuity, i.e., visual similarity across adjacent frames, we use powerful multimodal large language models (MLLMs) to annotate each frame within initial pseudo boundaries.
arXiv Detail & Related papers (2023-12-05T04:15:56Z) - AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach [50.855679274530615]
We present a novel domain-adaptive approach called AdaStereo to align multi-level representations for deep stereo matching networks.
Our models achieve state-of-the-art cross-domain performance on multiple benchmarks, including KITTI, Middlebury, ETH3D and DrivingStereo.
Our method is robust to various domain adaptation settings, and can be easily integrated into quick adaptation application scenarios and real-world deployments.
arXiv Detail & Related papers (2021-12-09T15:10:47Z) - Level Set Binocular Stereo with Occlusions [7.868449549351486]
Localizing stereo boundaries and predicting nearby disparities are difficult because stereo boundaries induce occluded regions where matching cues are absent.
This paper introduces an energy and level-set that improves boundaries by encoding occlusion geometry.
It can be implemented using messages that pass predominantly between parents and children in an undecimated hierarchy of image patches.
arXiv Detail & Related papers (2021-09-08T07:22:25Z) - SMD-Nets: Stereo Mixture Density Networks [68.56947049719936]
We propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures.
Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and precise disparity estimates near discontinuities.
We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets.
arXiv Detail & Related papers (2021-04-08T16:15:46Z) - Field of Junctions: Extracting Boundary Structure at Low SNR [5.584060970507507]
We introduce a bottom-up detector for simultaneously finding many boundary elements in an image, including contours, corners and junctions.
Notably, its analysis of contours, corners, junctions and uniform regions allows it to succeed at high noise levels, where other methods for boundary detection fail.
arXiv Detail & Related papers (2020-11-27T17:46:08Z) - StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization
of Domain Translation and Stereo Matching [56.95846963856928]
Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias.
We propose an end-to-end training framework with domain translation and stereo matching networks to tackle this challenge.
arXiv Detail & Related papers (2020-05-05T03:11:38Z) - AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching [50.06646151004375]
A novel domain-adaptive pipeline called AdaStereo aims to align multi-level representations for deep stereo matching networks.
Our AdaStereo models achieve state-of-the-art cross-domain performance on multiple stereo benchmarks, including KITTI, Middlebury, ETH3D, and DrivingStereo.
arXiv Detail & Related papers (2020-04-09T16:15:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.