Point Cloud Completion
75 papers with code • 3 benchmarks • 4 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
Latest papers
GPN: Generative Point-based NeRF
Scanning real-life scenes with modern registration devices typically gives incomplete point cloud representations, primarily due to the limitations of partial scanning, 3D occlusions, and dynamic light conditions.
CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers
easyry/cra-pcn
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Point cloud completion is an indispensable task for recovering complete point clouds due to incompleteness caused by occlusion, limited sensor resolution, etc.
LiDAR-based Person Re-identification
gwxuan/reid3d
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Camera-based person re-identification (ReID) systems have been widely applied in the field of public security.
VET: Visual Error Tomography for Point Cloud Completion and High-Quality Neural Rendering
lfranke/vet
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In our results, we show that our approach can improve the quality of a point cloud obtained by structure from motion and thus increase novel view synthesis quality significantly.
Fine-Tuning Generative Models as an Inference Method for Robotic Tasks
orrkrup/mace
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Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions.
PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion
To address this, we introduce PointSSC, the first cooperative vehicle-infrastructure point cloud benchmark for semantic scene completion.
LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models
kazuto1011/r2dm
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In this work, we present R2DM, a novel generative model for LiDAR data that can generate diverse and high-fidelity 3D scene point clouds based on the image representation of range and reflectance intensity.
DR-Pose: A Two-stage Deformation-and-Registration Pipeline for Category-level 6D Object Pose Estimation
zray26/dr-pose
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In the second stage, a novel registration network is designed to extract pose-sensitive features and predict the representation of object partial point cloud in canonical space based on the deformation results from the first stage.
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation
saltoricristiano/cosmix-uda
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In this study, we introduce compositional semantic mixing for point cloud domain adaptation, representing the first unsupervised domain adaptation technique for point cloud segmentation based on semantic and geometric sample mixing.
P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds
cuiruikai/partial2complete
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ICCV 2023
Point cloud completion aims to recover the complete shape based on a partial observation.
