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Point Cloud Completion: A Survey [PDF]

IEEE Transactions on Visualization and Computer Graphics
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.
Gary Tam, Muneeb I. Ahmad, Mark W Jones
exaly   +6 more sources

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Relationship-Based Point Cloud Completion

IEEE Transactions on Visualization and Computer Graphics, 2022
We propose a partial point cloud completion approach for scenes that are composed of multiple objects. We focus on pairwise scenes where two objects are in close proximity and are contextually related to each other, such as a chair tucked in a desk, a fruit in a basket, a hat on a hook and a flower in a vase.
Xi Zhao 0002, Bowen Zhang, Jinji Wu, Ruizhen Hu, Taku Komura   +4 more
openaire   +2 more sources

AdaPoinTr: Diverse Point Cloud Completion With Adaptive Geometry-Aware Transformers

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion. By representing
Jiwen Lu, Yongming Rao
exaly   +1 more source

Point cloud completion using extrusions

2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), 2012
In this paper, we propose modelling objects using extrusion-based representations, which can be used to complete partial point clouds. These extrusion-based representations are particularly well-suited for modelling basic household objects that robots will often need to manipulate. In order to efficiently complete a partial point cloud, we first detect
Oliver Kroemer, Heni Ben Amor, Marco Ewerton, Jan Peters 0001   +3 more
openaire   +1 more source

A point contextual transformer network for point cloud completion

Expert Systems With Applications
Liqiang Zhang
exaly   +3 more sources

Generative Modeling in Application to Point Cloud Completion

2020
The three-dimensional data representations have found numerous applications, most notably in SLAM and autonomous driving, where the most widely used type of data is a point cloud. In contrast to the image data, point clouds are unstructured objects, represented as sets of points in three- or six-dimensional (if the colors of surroundings are captured ...
Maciej Zamorski, Maciej Zieba, Jerzy Swiatek   +2 more
openaire   +2 more sources

SPC: Self-supervised point cloud completion

Neural Networks
Shape incompleteness is a common issue in point clouds acquired by depth sensors. Point cloud completion aims to restore partial point clouds to their complete form. However, most existing point cloud completion methods rely on complete point clouds or multi-view information of the same object during training, which is not practical for real-world ...
Jie Song, Xing Wu 0001, Junfeng Yao, Qi Zhang, Chenhao Shang, Quan Qian, Jun Song   +6 more
openaire   +3 more sources

Learning Point Cloud Completion without Complete Point Clouds: A Pose-Aware Approach

2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023
Jihun Kim, Hyeokjun Kwon, Yunseo Yang, Kuk-Jin Yoon   +3 more
openaire   +2 more sources

Structure-Aware Point Cloud Completion

2023
Zhihua Cheng, Xuejin Chen
openaire   +2 more sources