Self-Supervised Point Cloud Completion on Real Traffic Scenes Via Scene-Concerned Bottom-Up Mechanism

Ren, Yiming; Cong, Peishan; Zhu, Xinge; Ma, Yuexin

doi:10.1109/icme52920.2022.9860015

Cited by 3 publications

(6 citation statements)

References 18 publications

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“…ScanNet [109]: is an RGB-D video dataset containing 2.5 million crowd-sourced views and more than 1500 scans. They [36] [19] CD, F-score Some other datasets Pandar40 [119],3D-EPN [66],Trees [30] BuildingNet [120], nuScenes [25], 3D-future [102] CD Own dataset [11] CD are annotated with 3D camera poses, surface reconstructions, and instance-level semantic segmentations. Scan2CAD [110]: is an alignment dataset based on 1506 ScanNet scans with 97,607 annotated key points pairs between 14,225 (3049 unique) CAD models from ShapeNet and their counterpart objects in the scans.…”

Section: B Datasets For Learning-based Completion Methodsmentioning

confidence: 99%

“…Some completion algorithms perform the task on specific objects. For instance, Ren et al [25] completes scans of vehicles in real traffic scenes by using a vehicle memory bank of point cloud frames created based on symmetry and similarity of vehicles. Completion for applications in specific fields such as autonomous driving [26], [27], medical field [28], [29], agriculture [30] [31] might require point clouds from specific objects.…”

Section: A Inputsmentioning

confidence: 99%

“…Shi et al [52] use temporal information in the form of a sequence of unaligned sparse inputs to enhance completion. Similarly, Ren et al [25] use symmetry and similarity of vehicles as supplemental information. Katsen et al [53] use a textual description of an object with PPC.…”

Section: A Inputsmentioning

confidence: 99%

“…The latent representations from similar structured point clouds is used as complementary features to produce a complete output. Overall, leveraging external information to improve completion performance [25], [35] is becoming more frequent.…”

Section: A Inputsmentioning

confidence: 99%

“…ACL-SPC [104] uses a self-supervised adaptive control-loop framework that only uses a single partial input and no prior information. Ren et al [25] use self-supervised completion for vehicles in real traffic scenes. All unsupervised methods mentioned so far assume a one-to-one deterministic approach that maps a partial shape to a latent code and then the code to a complete shape [63].…”

Section: B Learning-based Approachmentioning

confidence: 99%

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Point Cloud Completion: A Survey

Tesema,

Hill,

Jones

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Point cloud completion is the task of producing a complete 3D shape given an input of a partial point cloud. It has become a vital process in 3D computer graphics, vision and applications such as autonomous driving, robotics, and augmented reality. These applications often rely on the presence of a complete 3D representation of the environment. Over the past few years, many completion algorithms have been proposed and a substantial amount of research has been carried out. However, there are not many in-depth surveys that summarise the research progress in such a way that allows users to make an informed choice of what algorithms to employ given the type of data they have, the end result they want, the challenges they may face and the possible strategies they could use. In this study, we present a comprehensive survey and classification of papers on point cloud completion untill August 2023 based on the strategies, techniques, inputs, outputs, and network architectures. We will also cover datasets, evaluation methods, and application areas in point cloud completion. Finally, we discuss challenges faced by the research community and future research directions.

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