Masked Autoencoders for Point Cloud Self-supervised Learning
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AGI represents visionary goal of crafting AI systems that emulate comprehensive and multifaceted aspects of human cognition, focused on developing AI with capability for holistic understanding and complex reasoning closely aligning with depth and breadth of human cognitive abilities.
“AGI, representing the visionary goal of crafting AI systems that emulate the comprehensive and multifaceted aspects of human cognition, is a subdomain focused on developing AI with the capability for holistic understanding and complex reasoning that closely aligns with the depth and breadth of human cognitive abilities [282], [283], [32].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [282]
AGI involves not just replicating human intelligence but also crafting systems that can autonomously perform a variety of tasks, demonstrating adaptability and learning capabilities akin to humans.
“AGI is not just about replicating human intelligence, but also involves crafting systems that can autonomously perform a variety of tasks, demonstrating adaptability and learning capabilities akin to those of humans [282], [283].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [282]
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Self-citationA Point Set Generation Network for 3D Object Reconstruction from a Single Image
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- Authors
- Pang, Y., Wang, W., Tay, F. E. H., Liu, W., Tian, Y., & Yuan, L.
- Affiliations
- National University of Singapore (SG)
- Zhejiang University (CN)
- Tencent (China) (CN)
- Peking University (CN)
- Corresponding author
- Yuan, Li
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 604-621
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4312788538
- Cited by
- 605
- References
- 34
- Citations by year
- 2026: 110 · 2025: 197 · 2024: 187 · 2023: 105 · 2022: 6
- FWCI
- 93.79
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- masked autoencoder, point clouds, self-supervised learning, transformer-based autoencoder, few-shot classification, 3D shape classification
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