"Continuous 3D Perception Model with Persistent State"
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https://arxiv.org/abs/2501.12387
Traditional 3D reconstruction methods process each scene from scratch. These methods struggle with limited views or dynamic content.
This paper introduces a continuous 3D perception model. It uses a persistent state to incrementally build a 3D scene understanding from image streams.
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📌 CUT3R uses recurrent Transformers for online 3D perception. Stateful processing enables accumulation of scene understanding from sequential views. This contrasts with stateless, per-frame methods.
📌 The model's strength lies in its persistent state. It allows for implicit multi-view fusion and temporal coherence. This is achieved without explicit global alignment, unlike prior pairwise approaches.
📌 Querying the state with raymaps is a key innovation. It demonstrates the model's ability to extrapolate and infer unseen 3D scene structure from learned priors.
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Methods Explored in this Paper 🔧:
→ This paper presents CUT3R. CUT3R is a Continuous Updating Transformer for 3D Reconstruction.
→ CUT3R uses a recurrent model. It processes image streams sequentially.
→ A persistent state, represented by tokens, stores scene information. This state is initialized with learnable tokens.
→ For each new image, a Vision Transformer encoder extracts image features. These features interact with the persistent state using Transformer decoders.
→ This interaction involves two processes: state update and state readout. State update integrates new image information into the state. State readout retrieves past context for predictions.
→ After interaction, the model predicts pointmaps in camera and world coordinates. It also predicts camera poses.
→ The model can also query the state with virtual views represented as raymaps. This allows inference of unseen scene parts.
→ The model is trained on diverse datasets. These datasets include static and dynamic scenes, videos, and image collections. Curriculum learning is used during training.
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Key Insights 💡:
→ Integrating data-driven priors with a recurrent state enables continuous 3D perception.
→ The persistent state allows the model to refine 3D reconstructions over time with more observations.
→ The model can infer 3D structures in unobserved areas by querying its internal state.
→ The model effectively handles dynamic scenes and sparse image inputs.
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Results 📊:
→ Achieves 0.063 AbsRel error and 96.2% delta < 1.25 accuracy on Bonn dataset for monocular depth estimation.
→ Outperforms DUSt3R in video depth estimation on KITTI dataset with 0.118 AbsRel error at 16.58 FPS.
→ Achieves 0.213 ATE on Sintel dataset for camera pose estimation, performing best among online methods.
→ Obtains 0.126 Acc and 0.154 Comp on 7-Scenes dataset for 3D reconstruction, comparable to offline methods but at 17 FPS.