Progressive-X+: Clustering in the Consensus Space

Baráth, Dániel; Rozumny, Denys; Eichhardt, Ivan; Hajder, Levente; Matas, Jiřı́

doi:10.48550/arxiv.2103.13875

Cited by 2 publications

(4 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RANSACbased methods(e.g. [28] [9] [10] [35] [29] [11]) run revised RANSAC sequentially to obtain multiple model parameters. They change the sampling weight of each point in each iteration to get different model parameters.…”

Section: Related Workmentioning

confidence: 99%

“…However, this approach needs to train a detector or a segementation network [39] [25] for specific objects or classes, which does not apply to unknown objects or ar-bitrary 3D scans. Another solution is via multi-model fitting [32] [33] [34] or [35] [29] [11]. Existing multi-model fitting methods rely on sampling valid hypotheses, which involves a large number of sampling steps when the number of models or the outlier ratio becomes high, making the efficiency and robustness of those algorithms drop drastically.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-instance Point Cloud Registration by Efficient Correspondence Clustering

Tang¹,

Zou²

2021

Preprint

View full text Add to dashboard Cite

We address the problem of estimating the poses of multiple instances of the source point cloud within a target point cloud. Existing solutions require sampling a lot of hypotheses to detect possible instances and reject the outliers, whose robustness and efficiency degrade notably when the number of instances and outliers increase. We propose to directly group the set of noisy correspondences into different clusters based on a distance invariance matrix. The instances and outliers are automatically identified through clustering. Our method is robust and fast. We evaluated our method on both synthetic and real-world datasets. The results show that our approach can correctly register up to 20 instances with an F1 score of 90.46% in the presence of 70% outliers, which performs significantly better and at least 10× faster than existing methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-instance Point Cloud Registration by Efficient Correspondence Clustering

Tang¹,

Zou²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…CBS [111], DGSAC [1], P-x [112], P-xp [113] and MLink [114]. In all cases we ran the methods in the same platform (PC with 2.7GHz Intel core i7 processor) using code publicly available or provided by the authors (except for DGSAC for which we report values from [1].…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Conversely, T-Linkage, RCM, DGSAC, Prog-x, Prog-xp, and MLink estimate this number but require an inlier threshold, which may depend on the dataset. Prog-x [112] is implemented in Python, Prog-xp [113] in C++, and the rest in Matlab.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Fast semi-algebraic clustering for efficient system identification and geometric scene understanding

Ozbay

View full text Add to dashboard Cite

I would like to begin by expressing my deepest gratitude to my advisor, Prof. Mario Sznaier. It was a privilege to work under his guidance throughout my PhD, learning not only how to conduct academic research but also how to tackle tough problems and brainstorm innovative solutions, including the occasional "Yet Another Stupid Idea" (YASI) when faced with unexpected challenges. I am particularly grateful for his encouragement to tackle tough problems and teaching me "Problems worthy of attack prove their worth by fighting back.". Additionally, I would like to thank Prof. Octavia Camps for her domain expertise in the computer vision field and her invaluable contributions to my research. It was a pleasure to work under her guidance and in collaboration with her team to learn about interesting problems in computer vision.I am thankful to my committee members, Prof. Taskin Padir and Prof. Rifat Sipahi, for their invaluable guidance and support. Prof. Padir's passion for robotics and his proactive initiatives was very inspiring. His robotics class provided me with extensive knowledge in the field. Additionally, collaborating with Prof. Sipahi in both academic and extracurricular endeavors has been enriching. I appreciate his support and his efforts in nurturing a sense of community among Turkish students at the university.My heartfelt appreciation goes to my family for their unwavering support and encouragement. My parents have been a constant source of strength, guiding me through challenges and celebrating my achievements. I am also grateful to my sister, who has been a faithful companion on this journey, offering a listening ear and unwavering support. It was a precious gift to have somebody by my side who went through the same process. I am grateful for their belief in me, which has been a driving force during difficult times. Special thanks to my sister and my brother-in-law for being like a second family to me in the US.Finally, I am deeply grateful to my fiancée, Ugurcan Ozdemir for literally being right by my side through all the worst and the best days since he entered my life. His support, constant presence, and unshakeable belief in me, especially during moments of doubt, have been invaluable. He has not only been a sounding board for navigating tough decisions but also provided technical support when needed. To my cherished friends who have become like family, especially

show abstract

Progressive-X+: Clustering in the Consensus Space

Cited by 2 publications

References 53 publications

Multi-instance Point Cloud Registration by Efficient Correspondence Clustering

Multi-instance Point Cloud Registration by Efficient Correspondence Clustering

Fast semi-algebraic clustering for efficient system identification and geometric scene understanding

Contact Info

Product

Resources

About