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MHE-Former: Multi-Hypothesis Transformers via Entropy Maximization for 3D Mesh Recovery
Not provided in the abstract
multimodal3D reconstructionmachine learningtransformers
2609.10743
Builder Relevance
4h ago80%
Abstract
This paper introduces MHE-Former, a Transformer-based framework for improving monocular 3D mesh recovery through multi-hypothesis learning and selection.
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Core Claim
MHE-Former achieves state-of-the-art performance in accuracy and diversity for 3D mesh recovery by utilizing a multi-hypothesis approach and context-aware hypothesis selection.
Method / Result
The framework demonstrates significant improvements in both accuracy and diversity across multiple datasets.
Limitations
The abstract does not specify any limitations or reproducibility concerns.
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