A Novel Confidence Measure for Disparity Maps by Pixel-Wise Cost Function Analysis

Veld, Ron op het; Jaschke, Tobias; Bätz, Michel; Palmieri, Luca; Keinert, Joachim

doi:10.1109/icip.2018.8451500

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…These methods developed algorithms based on the minimum cost and local properties of the cost profile (Egnal, Mintz, and Wildes 2004;Haeusler, Nair, and Kondermann 2013;Haeusler and Klette 2012;Wedel et al 2009;Kim, Yoo, and Kim 2014;Kim, Jang, and Kim 2016). Some conventional methods examine the entire curve of the cost profile to extract useful informa-tion for measuring confidence (Haeusler, Nair, and Kondermann 2013;Matthies 1992;Scharstein and Szeliski 1996;Het Veld et al 2018). Unlike these methods that concentrate on the cost profile inside the stereo-matching model, the proposed approach examines the disparity profile outside the stereo-matching network, making it suitable for learningbased end-to-end stereo-matching methods.…”

Section: Conventional Methodsmentioning

confidence: 99%

Modeling Stereo-Confidence out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep

Lee,

Ka,

Choi

et al. 2024

AAAI

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We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learning-based approaches, especially in safety-critical systems. Grounded in the foundational concepts of disparity definition and the disparity plane sweep, the proposed stereo-confidence method is built upon the idea that any shift in a stereo-image pair should be updated in a corresponding amount shift in the disparity map. Based on this idea, the proposed stereo-confidence method can be summarized in three folds. 1) Using the disparity plane sweep, multiple disparity maps can be obtained and treated as a 3-D volume (predicted disparity volume), like the cost volume is constructed. 2) One of these disparity maps serves as an anchor, allowing us to define a desirable (or ideal) disparity profile at every spatial point. 3) By comparing the desirable and predicted disparity profiles, we can quantify the level of matching ambiguity between left and right images for confidence measurement. Extensive experimental results using various stereo-matching networks and datasets demonstrate that the proposed stereo-confidence method not only shows competitive performance on its own but also consistent performance improvements when it is used as an input modality for learning-based stereo-confidence methods.

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Section: Conventional Methodsmentioning

confidence: 99%

Modeling Stereo-Confidence out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep

Lee,

Ka,

Choi

et al. 2024

AAAI

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show abstract

“…Properties of matching cost curve (Egnal et al, 2004;Hirschmüller et al, 2002;Veld et al, 2018;Zhang and Shan, 2001) 1. Rich information and characteristics could be extracted from the cost curves.…”

Section: Metric Strengths Weaknessesmentioning

confidence: 99%

A Critical Analysis of Internal Reliability for Uncertainty Quantification of Dense Image Matching in Multi-View Stereo

Huang,

Qin

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Nowadays, photogrammetrically derived point clouds are widely used in many civilian applications due to their low cost and flexibility in acquisition. Typically, photogrammetric point clouds are assessed through reference data such as LiDAR point clouds. However, when reference data are not available, the assessment of photogrammetric point clouds may be challenging. Since these point clouds are algorithmically derived, their accuracies and precisions are highly varying with the camera networks, scene complexity, and dense image matching (DIM) algorithms, and there is no standard error metric to determine per-point errors. The theory of internal reliability of camera networks has been well studied through first-order error estimation of Bundle Adjustment (BA), which is used to understand the errors of 3D points assuming known measurement errors. However, the measurement errors of the DIM algorithms are intricate to an extent that every single point may have its error function determined by factors such as pixel intensity, texture entropy, and surface smoothness. Despite the complexity, there exist a few common metrics that may aid the process of estimating the posterior reliability of the derived points, especially in a multi-view stereo (MVS) setup when redundancies are present. In this paper, by using an aerial oblique photogrammetric block with LiDAR reference data, we analyze several internal matching metrics within a common MVS framework, including statistics in ray convergence, intersection angles, DIM energy, etc. We associate these metrics to the per-point errors evaluated through LiDAR reference data and discuss their potential contributions in estimating internal reliabilities of point clouds derived from DIM algorithms. The experimental results show that ray convergence and DIM energy are relevant indicators for the accuracy of the generated point clouds. Initial investigation shows that these two indicators could be further utilized to infer the measurement errors without reference data, which could potentially estimate the reliabilities of point clouds through error propagation.

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On the confidence of stereo matching in a deep-learning era: a quantitative evaluation

Poggi

Kim

Tosi

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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A Novel Confidence Measure for Disparity Maps by Pixel-Wise Cost Function Analysis

Cited by 4 publications

References 28 publications

Modeling Stereo-Confidence out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep

Modeling Stereo-Confidence out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep

A Critical Analysis of Internal Reliability for Uncertainty Quantification of Dense Image Matching in Multi-View Stereo

On the confidence of stereo matching in a deep-learning era: a quantitative evaluation

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