Fast and Robust Registration of Partially Overlapping Point Clouds

Arnold, Eduardo; Mozaffari, Sajjad; Dianati, Mehrdad

doi:10.1109/lra.2021.3137888

Cited by 38 publications

(16 citation statements)

References 28 publications

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“…In the past decade, the attention mechanism has played an increasingly important role in computer vision [13,57,69,70], which also inspires collaborative perception research. Since feature selection and relationship exploring are vital issues in intermediate collaborative perception, some works [21,23,29,71,72,73,74] leverage attention mechanisms to exploit more dynamic and robust collaborative perception strategies. Due to flexibility, attention-based design becomes the dominant strategy in intermediate collaboration.…”

Section: A Improve Collaboration Efficiency and Performancementioning

confidence: 99%

Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Han¹,

Zhang²,

Li³

et al. 2023

Preprint

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Section: A Improve Collaboration Efficiency and Performancementioning

confidence: 99%

Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Han¹,

Zhang²,

Li³

et al. 2023

Preprint

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“…A comprehensive survey of data-driven feature learning methods can be found in [42], including works up to 2021. More recent methods [22], [3] and [24] try to overcome the problem of registration of point clouds with low overlap. The approach in [22] enhances the quality of the correspondences, in a regime with low overlap, by using a graph-based self-and cross-attention network.…”

Section: B Learning-based Point Cloud Registrationmentioning

confidence: 99%

“…More recent methods [22], [3] and [24] try to overcome the problem of registration of point clouds with low overlap. The approach in [22] enhances the quality of the correspondences, in a regime with low overlap, by using a graph-based self-and cross-attention network. PREDATOR [3] introduces a novel overlap-attention block that aims to focus more on the overlapping parts of point cloud pairs.…”

Section: B Learning-based Point Cloud Registrationmentioning

confidence: 99%

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Point Cloud Registration With Object-Centric Alignment

et al. 2022

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Point cloud registration is a core task in 3D perception, which aims to align two point clouds. Moreover, the registration of point clouds with low overlap represents a harder challenge, where previous methods tend to fail. Recent deep learning-based approaches attempt to overcome this issue by learning to find overlapping regions in the whole scene. However, they still lack robustness and accuracy, and thus might not be suitable for real-world applications. Therefore, we present a novel registration pipeline that focuses on object-level alignment to provide a robust and accurate alignment of point clouds. By extracting and completing the missing points of the object of interest, a rough alignment can be achieved even for point clouds with low overlap captured from widely apart viewpoints. We provide a quantitative and qualitative evaluation on synthetic and real-world data captured with a Kinect v2. The proposed approach outperforms the current the current state-of-the-art methods by more than 29% w.r.t. the registration recall on the introduced synthetic dataset. We show that the overall performance and robustness increases due to the object-level alignment, while the baselines perform poorly as they take the entire scene into account.

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“…Recent works are also studying synthetic data generation for CP [14], [15]. For example, the open-source CARLA simulator [16] is used to simulate the traffic environment and generate synthetic data.…”

Section: Related Workmentioning

confidence: 99%

CoPEM: Cooperative Perception Error Models for Autonomous Driving

Piazzoni

Cherian

Vijay

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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In this paper, we introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective and efficient integration of V2X solutions within a virtual test environment. We focus our analysis on the occlusion problem in the (onboard) perception of Autonomous Vehicles (AV), which can manifest as misdetection errors on the occluded objects. Cooperative perception (CP) solutions based on Vehicle-to-Everything (V2X) communications aim to avoid such issues by cooperatively leveraging additional points of view for the world around the AV. This approach usually requires many sensors, mainly cameras and LiDARs, to be deployed simultaneously in the environment either as part of the road infrastructure or on other traffic vehicles. However, implementing a large number of sensor models in a virtual simulation pipeline is often prohibitively computationally expensive. Therefore, in this paper, we rely on extending Perception Error Models (PEMs) to efficiently implement such cooperative perception solutions along with the errors and uncertainties associated with them. We demonstrate the approach by comparing the safety achievable by an AV challenged with a traffic scenario where occlusion is the primary cause of a potential collision.

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Fast and Robust Registration of Partially Overlapping Point Clouds

Abstract: How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 38 publications

References 28 publications

Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Point Cloud Registration With Object-Centric Alignment

CoPEM: Cooperative Perception Error Models for Autonomous Driving

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