"Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning"

Unlock 3D vision across datasets without endless finetuning.

Multiview equivariance: The ingredient for generalizable 3D correspondence.

This paper addresses the challenge of improving 3D correspondence understanding by proposing a multiview equivariant approach with minimal feature finetuning, enhancing generalization across diverse datasets.

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Paper - https://arxiv.org/abs/2411.19458

Original Problem 😕:

→ Existing 3D correspondence methods struggle to generalize across different datasets.

→ These methods often require extensive finetuning for new datasets.

→ This finetuning process is computationally expensive and data-intensive.

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Solution in this Paper 💡:

→ This paper introduces multiview equivariance to enhance 3D correspondence learning.

→ The method leverages geometric transformations across multiple views of 3D shapes.

→ It enforces consistency in feature representations under these transformations.

→ This is achieved through a novel training strategy that encourages features to be equivariant to multiview transformations.

→ Minimal feature finetuning is needed by pretraining features with multiview equivariance.

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Key Insights from this Paper 🤔:

→ Multiview equivariance is crucial for learning robust and generalizable 3D correspondence features.

→ Pretraining features with multiview equivariance significantly reduces the need for extensive dataset-specific finetuning.

→ Equivariant features capture intrinsic 3D shape properties that are invariant to viewpoint changes.

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Results 🎉:

→ Achieves state-of-the-art performance on multiple 3D correspondence benchmarks after minimal finetuning.

→ Shows significant improvement in generalization ability across diverse datasets compared to existing methods.

→ Demonstrates superior performance even with limited finetuning data for new datasets.