SC-PROSAC: An Improved Progressive Sample Consensus Algorithm Based on Spectral Clustering

Ma, Shuhua; Ma, Xianchun; You, Hairong; Tang, Tiang; Wang, Jinhua; Wang, Min

doi:10.1109/icrcv52986.2021.9546964

Cited by 4 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we adopt the 6D pose fitting module proposed in EPOS [25] for the final 6D pose computation. It adopts the PROSAC algorithm [38] instead of RANSAC [19] to calculate the final 6D pose. This algorithm is a locally optimized RANSAC that firstly focuses on correspondences with higher confidence and progressively turns to uniform sampling.…”

Section: D Pose Computationmentioning

confidence: 99%

Unseen Object 6D Pose Estimation: A Benchmark and Baselines

Gou¹,

Pan²,

Fang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Estimating the 6D pose for unseen objects is in great demand for many real-world applications. However, current state-of-the-art pose estimation methods can only handle objects that are previously trained. In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing. We collect a dataset with both real and synthetic images and up to 48 unseen objects in the test set. In the mean while, we propose a new metric named Infimum ADD (IADD) which is an invariant measurement for objects with different types of pose ambiguity. A two-stage baseline solution for this task is also provided. By training an endto-end 3D correspondences network, our method finds corresponding points between an unseen object and a partial view RGBD image accurately and efficiently. It then calculates the 6D pose from the correspondences using an algorithm robust to object symmetry. Extensive experiments show that our method outperforms several intuitive baselines and thus verify its effectiveness. All the data, code and models will be made publicly available. Project page: www.graspnet.net/unseen6d

show abstract

Section: D Pose Computationmentioning

confidence: 99%

Unseen Object 6D Pose Estimation: A Benchmark and Baselines

Gou¹,

Pan²,

Fang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, the outlier rejection phase is mandatory for the accurate fitting of the transformation model. Some robust probabilistic models such as Random Sample Consensus (RANSAC) [ 10 ], Progressive Sample Consensus (PROSAC) [ 11 ], and M-estimator Sample Consensus (MSAC) [ 8 ] can be used for outlier rejection in matched features and for fitting the transformation model.…”

Section: Introductionmentioning

confidence: 99%

Automatic Sequential Stitching of High-Resolution Panorama for Android Devices Using Precapture Feature Detection and the Orientation Sensor

Yaseen

Kwon²,

Lee³

et al. 2023

Sensors

View full text Add to dashboard Cite

Image processing on smartphones, which are resource-limited devices, is challenging. Panorama generation on modern mobile phones is a requirement of most mobile phone users. This paper presents an automatic sequential image stitching algorithm with high-resolution panorama generation and addresses the issue of stitching failure on smartphone devices. A robust method is used to automatically control the events involved in panorama generation from image capture to image stitching on Android operating systems. The image frames are taken in a firm spatial interval using the orientation sensor included in smartphone devices. The features-based stitching algorithm is used for panorama generation, with a novel modification to address the issue of stitching failure (inability to find local features causes this issue) when performing sequential stitching over mobile devices. We also address the issue of distortion in sequential stitching. Ultimately, in this study, we built an Android application that can construct a high-resolution panorama sequentially with automatic frame capture based on an orientation sensor and device rotation. We present a novel research methodology (called “Sense-Panorama”) for panorama construction along with a development guide for smartphone developers. Based on our experiments, performed by Samsung Galaxy SM-N960N, which carries system on chip (SoC) as Qualcomm Snapdragon 845 and a CPU of 4 × 2.8 GHz Kyro 385, our method can generate a high-resolution panorama. Compared to the existing methods, the results show improvement in visual quality for both subjective and objective evaluation.

show abstract