GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision
To address this gap, we propose GeoBridge, an evolutionary framework for transferable decoder topologies.
ProofPaper ↗
Key points
- Depth estimation and Gaussian reconstruction share a need for multi-scale feature processing, yet use different prediction interfaces and training objectives.
- Whether a decoder's internal computation can be optimized once and reused across these systems remains a structural design question.
- GeoBridgeNet-A3 achieves the best depth accuracy among four adapted decoder baselines under a common encoder and training protocol on NYU Depth V2 and KITTI.
- These results support decoder topology as a reusable design object across the evaluated geometric vision systems.
Sources (1)
- [1]GeoBridge: Evolving Transferable Decoder Topologies for Geometric VisionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 02:23 AM
To address this gap, we propose GeoBridge, an evolutionary framework for transferable decoder topologies.
Depth estimation and Gaussian reconstruction share a need for multi-scale feature processing, yet use different prediction interfaces and training objectives.
Extractive summary: sentences quoted from the sources.
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