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Survey of Latest Advancements in Deep Learning for Point Cloud Completion
IEEE Transactions on Visualization and Computer Graphics
|February 26, 2026
Summary
This survey reviews deep neural networks for point cloud completion, addressing incomplete 3D scans from sensors. It analyzes recent advancements and discusses future research directions in this computer vision field.
Area of Science:
- Computer Vision
- 3D Data Processing
- Machine Learning
Background:
- Point clouds are crucial for robotics and autonomous driving.
- Incomplete scans due to sensor limitations are a major challenge.
- Point cloud completion is vital for reconstructing 3D object geometry.
Purpose of the Study:
- To provide a comprehensive survey of recent deep neural network advancements for point cloud completion.
- To analyze methods published between 2024 and December 2025.
- To offer insights into future research directions.
Main Methods:
- Reviewing foundational methods up to 2023.
- Analyzing recent deep neural network components for improvement.
- Comparative performance analysis on benchmark datasets.
Main Results:
- Identification of key advancements in deep learning for point cloud completion.
- Performance evaluation of various methods on standard datasets.
- Discussion of current limitations and future research opportunities.
Conclusions:
- Deep learning methods have significantly advanced point cloud completion.
- Further research is needed to address remaining challenges.
- This survey provides a roadmap for future developments in the field.
