Point Cloud Completion
95 papers with code • 3 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Point Cloud Completion
| Trend | Dataset | Best Model | Paper | Code | Compare |
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ShapeNet
Completion3D
MVP
ShapeNet-ViPC
VBR
Most implemented papers
Pointer Networks
devsisters/pointer-network-tensorflow
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NeurIPS 2015
It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output.
PCN: Point Completion Network
wentaoyuan/pcn
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Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications.
SPCNet: Stepwise Point Cloud Completion Network
code-implementation1/Code8
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We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings.
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
We introduce a method for learning to generate the surface of 3D shapes.
Unpaired Point Cloud Completion on Real Scans using Adversarial Training
xuelin-chen/pcl2pcl-gan-pub
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ICLR 2020
As 3D scanning solutions become increasingly popular, several deep learning setups have been developed geared towards that task of scan completion, i. e., plausibly filling in regions there were missed in the raw scans.
Morphing and Sampling Network for Dense Point Cloud Completion
Colin97/MSN-Point-Cloud-Completion
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3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community.
PF-Net: Point Fractal Network for 3D Point Cloud Completion
zztianzz/PF-Net-Point-Fractal-Network
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CVPR 2020
Unlike existing point cloud completion networks, which generate the overall shape of the point cloud from the incomplete point cloud and always change existing points and encounter noise and geometrical loss, PF-Net preserves the spatial arrangements of the incomplete point cloud and can figure out the detailed geometrical structure of the missing region(s) in the prediction.
Refinement of Predicted Missing Parts Enhance Point Cloud Completion
ivansipiran/Refinement-Point-Cloud-Completion
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This paper proposes an end-to-end neural network architecture that focuses on computing the missing geometry and merging the known input and the predicted point cloud.
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer
allenxiangx/snowflakenet
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ICCV 2021
However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it hard to reveal fine local geometric details on the complete shape.
KTNet: Knowledge Transfer for Unpaired 3D Shape Completion
MindSpore-paper-code-3/code9
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The student network takes the incomplete one as input and restores the corresponding complete shape.
