Quantum Robust Fitting

Chin, Tat-Jun; Suter, David; Chng, Shin-Fang; Quach, James Q.

doi:10.1007/978-3-030-69525-5_29

Cited by 4 publications

(15 citation statements)

References 15 publications

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“…Second, their algorithm is based on the gate computing model, which is fundamentally different from the quantum annealing approach adopted in our work. Third, the results in [19] are only based on simulation; we will compare against [19] on this basis in Sec. 6.…”

Section: Related Workmentioning

confidence: 99%

“…Following [19], we consider residuals r i (x) that are quasiconvex, which encapsulates many geometric models of interest in computer vision [41]. Formally, if the set…”

Section: Preliminariesmentioning

confidence: 99%

“…At its core, our algorithm aims to solve (VC), i.e., find the minimum outlier set, but by incrementally generating the hyperedges A ⊆ E. Other main characteristics of the algorithm are: • At each iteration, the QUBO (19) based on the current hyperedges A is solved using quantum annealing. • The penalty λ for (19) decays following a schedule defined by hyperparameters λ, γ and M (Step 6). • Hyperedges are sampled from a candidate vertex set V , which is updated based on the current results (Sec.…”

Section: Main Algorithmmentioning

confidence: 99%

“…1 on synthetic 1D linear regression instances with N = 20, 50, and 100 points, each with outlier ratio 0.2. The QUBO subroutine (19) in the main algorithm was solved using the QPU with λ = 1.0 (no λ decay was done). The values z 1 and LP (A) were plotted as a function of the size of A, i.e., number of hyperedges.…”

Section: Synthetic Datamentioning

confidence: 99%

“…4 shows the runtime of QA and SA across the iterations of Alg. 1 (for QA, the cost of embedding (19) onto the QPU was excluded; again, see Sec. F in the supp.…”

Section: Synthetic Datamentioning

confidence: 99%

See 4 more Smart Citations

A Hybrid Quantum-Classical Algorithm for Robust Fitting

Doan¹,

Sasdelli²,

Suter³

et al. 2022

Preprint

Self Cite

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Fitting geometric models onto outlier contaminated data is provably intractable. Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do not provide optimality guarantees and error bounds. It is therefore critical to develop novel approaches that can bridge the gap between exact solutions that are costly, and fast heuristics that offer no quality assurances. In this paper, we propose a hybrid quantum-classical algorithm for robust fitting. Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs and terminates with a global solution or an error bound. The combinatorial subproblems are amenable to a quantum annealer, which helps to tighten the bound efficiently. While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics. Moreover, our work represents a concrete application of quantum computing in computer vision. We present results obtained using an actual quantum computer (D-Wave Advantage) and via simulation 1 .

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Section: Related Workmentioning

confidence: 99%

“…Following [19], we consider residuals r i (x) that are quasiconvex, which encapsulates many geometric models of interest in computer vision [41]. Formally, if the set…”

Section: Preliminariesmentioning

confidence: 99%

Section: Main Algorithmmentioning

confidence: 99%

Section: Synthetic Datamentioning

confidence: 99%

“…4 shows the runtime of QA and SA across the iterations of Alg. 1 (for QA, the cost of embedding (19) onto the QPU was excluded; again, see Sec. F in the supp.…”

Section: Synthetic Datamentioning

confidence: 99%

See 3 more Smart Citations

A Hybrid Quantum-Classical Algorithm for Robust Fitting

Doan¹,

Sasdelli²,

Suter³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

A Hybrid Quantum-Classical Algorithm for Robust Fitting

Doan

Sasdelli

Suter

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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3D LiDAR SLAM: A survey

Zhang,

Shi,

2024

The Photogrammetric Record

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Simultaneous localization and mapping (SLAM) is a very challenging yet fundamental problem in the field of robotics and photogrammetry, and it is also a prerequisite for intelligent perception of unmanned systems. In recent years, 3D LiDAR SLAM technology has made remarkable progress. However, to the best of our knowledge, almost all existing surveys focus on visual SLAM methods. To bridge the gap, this paper provides a comprehensive review that summarizes the scientific connotation, key difficulties, research status, and future trends of 3D LiDAR SLAM, aiming to give readers a better understanding of LiDAR SLAM technology, thereby inspiring future research. Specifically, it summarizes the contents and characteristics of the main steps of LiDAR SLAM, introduces the key difficulties it faces, and gives the relationship with existing reviews; it provides an overview of current research hotspots, including LiDAR‐only methods and multi‐sensor fusion methods, and gives milestone algorithms and open‐source tools in each category; it summarizes common datasets, evaluation metrics and representative commercial SLAM solutions, and provides the evaluation results of mainstream methods on public datasets; it looks forward to the development trend of LiDAR SLAM, and considers the preliminary ideas of multi‐modal SLAM, event SLAM, and quantum SLAM.

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Quantum Robust Fitting

Cited by 4 publications

References 15 publications

A Hybrid Quantum-Classical Algorithm for Robust Fitting

A Hybrid Quantum-Classical Algorithm for Robust Fitting

A Hybrid Quantum-Classical Algorithm for Robust Fitting

3D LiDAR SLAM: A survey

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