TTT3R: 3D Reconstruction as Test-Time Training (2509.26645v1)

Abstract: Modern Recurrent Neural Networks have become a competitive architecture for 3D reconstruction due to their linear-time complexity. However, their performance degrades significantly when applied beyond the training context length, revealing limited length generalization. In this work, we revisit the 3D reconstruction foundation models from a Test-Time Training perspective, framing their designs as an online learning problem. Building on this perspective, we leverage the alignment confidence between the memory state and incoming observations to derive a closed-form learning rate for memory updates, to balance between retaining historical information and adapting to new observations. This training-free intervention, termed TTT3R, substantially improves length generalization, achieving a $2\times$ improvement in global pose estimation over baselines, while operating at 20 FPS with just 6 GB of GPU memory to process thousands of images. Code available in https://rover-xingyu.github.io/TTT3R

• The paper introduces TTT3R, a novel test-time training approach that reframes state updates as an online learning problem to overcome catastrophic forgetting in 3D reconstruction models.
• It employs a per-token, confidence-guided learning rate to adaptively update states, significantly improving generalization and efficiency (20 FPS) over long video sequences.
• Empirical evaluations on datasets like ScanNet and TUM-D demonstrate a 2× improvement in pose estimation and competitive 3D reconstruction quality compared to resource-intensive offline methods.

Test-Time Training for Online 3D Reconstruction: The TTT3R Framework

The TTT3R framework introduces a principled, training-free approach to online 3D reconstruction, addressing the catastrophic forgetting problem in recurrent neural architectures for long video sequences. By recasting the state update mechanism as a test-time training (TTT) process, TTT3R leverages internal alignment confidence to adaptively control memory updates, enabling robust length generalization and efficient inference over thousands of frames.

Figure 1: Sequence modeling layers: full attention (quadratic cost), vanilla RNN (linear cost, but suffers from forgetting), and TTT (test-time training) with adaptive fast weights for improved length generalization.

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Background: Sequence Modeling in 3D Reconstruction

Modern 3D reconstruction foundation models predict camera poses and scene geometry from sequences of RGB images. Transformer-based architectures, such as DUSt3R and VGGT, have demonstrated strong performance by leveraging global self-attention, but their computational and memory costs scale quadratically with sequence length, limiting their applicability to long video streams. RNN-based models, exemplified by CUT3R, offer constant memory and linear-time inference by maintaining a fixed-size state, but suffer from severe performance degradation as the number of input views increases due to state overfitting and catastrophic forgetting.

The core abstraction for these models is a sequence-to-sequence mapping:

• Each image is tokenized, and the state is updated with new observations.
• The updated state is used to predict per-frame 3D pointmaps, camera poses, and intrinsics.
• The update and read operations distinguish full attention (growing state, quadratic cost) from RNN-based (fixed state, linear cost) approaches.

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Test-Time Training Perspective and State Update Reformulation

TTT3R reframes the RNN state update as an online learning problem, inspired by recent advances in meta-learning and fast-weight architectures. The key insight is to treat the state as a set of fast weights, updated at each timestep via a gradient descent step, with the learning rate modulated by the alignment confidence between the current state and incoming observation.

The standard CUT3R update is:

$$\mathbf{S}_t = \mathbf{S}_{t-1} + \text{softmax}(\mathbf{Q}_{\mathbf{S}_{t-1}} \mathbf{K}_{\mathbf{X}_t}^\top) \mathbf{V}_{\mathbf{X}_t}$$

This formulation always prioritizes new information, leading to rapid forgetting of earlier frames.

TTT3R generalizes this by introducing a per-token, confidence-guided learning rate $\beta_t$:

$$\mathbf{S}_t = \mathbf{S}_{t-1} - \beta_t \nabla(\mathbf{S}_{t-1}, \mathbf{X}_t)$$

where $\nabla(\cdot)$ is a learned gradient function, and $\beta_t$ is derived from the cross-attention alignment between state and observation.

Figure 2: TTT3R pipeline: (a) vanilla CUT3R suffers from forgetting; (b) TTT3R introduces a confidence-guided state update, where alignment confidence modulates per-token learning rates.

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Confidence-Guided State Update Rule

The central contribution of TTT3R is the closed-form, confidence-guided state update rule. The per-token learning rate $\beta_t$ is computed as:

$$\beta_t = \sigma\left(\sum_{m} \mathbf{Q}_{\mathbf{S}_{t-1}} \mathbf{K}_{\mathbf{X}_t}^\top\right)$$

where $\sigma$ is a sigmoid activation, and the sum is over spatial dimensions. This learning rate acts as a soft gate, suppressing low-confidence updates (e.g., in textureless regions) and enabling robust memory retention over long sequences.

The final update rule is:

$$\mathbf{S}_t = \mathbf{S}_{t-1} - \beta_t \cdot \left[-\text{softmax}(\mathbf{Q}_{\mathbf{S}_{t-1}} \mathbf{K}_{\mathbf{X}_t}^\top) \mathbf{V}_{\mathbf{X}_t}\right]$$

This mechanism is training-free, requires no additional parameters or fine-tuning, and can be directly applied to existing CUT3R models.

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Empirical Evaluation

TTT3R is evaluated on standard benchmarks for camera pose estimation, video depth estimation, and 3D reconstruction, with a focus on long-sequence generalization and resource efficiency.

Camera Pose Estimation

On ScanNet and TUM-D datasets, TTT3R achieves a 2× improvement in global pose estimation over CUT3R, while maintaining real-time inference (20 FPS) and constant GPU memory usage (6 GB), even for sequences with thousands of frames.

Figure 3: Camera pose estimation accuracy as a function of sequence length; TTT3R maintains low error and constant memory, while baselines degrade or run out of memory.

Video Depth Estimation

TTT3R outperforms online baselines on both scale-invariant and metric depth metrics, particularly on long sequences where explicit memory methods (e.g., Point3R) suffer from memory exhaustion or degraded accuracy.

Figure 4: Scale-invariant relative depth evaluation on the Bonn dataset; TTT3R maintains high accuracy as sequence length increases.

3D Reconstruction

TTT3R delivers superior 3D reconstruction quality compared to online baselines, approaching the performance of full-attention offline methods (e.g., VGGT) but with orders-of-magnitude lower memory and compute requirements.

Figure 5: 3D reconstruction accuracy on the 7-scenes dataset; TTT3R outperforms online baselines and is competitive with offline methods.

Qualitative results further demonstrate that TTT3R mitigates catastrophic forgetting, enabling accurate geometry and loop closure in long video sequences.

Figure 6: Qualitative 3D reconstruction results; TTT3R preserves scene structure and avoids drift and ghosting artifacts present in CUT3R.

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Implementation and Deployment Considerations

• Integration: TTT3R is a plug-and-play modification for CUT3R and similar RNN-based 3D reconstruction models. The update rule can be implemented as a wrapper around the state update function, requiring only access to the cross-attention statistics.
• Resource Requirements: TTT3R operates at real-time speeds (20 FPS) and constant memory (6 GB GPU), independent of sequence length.
• Scalability: The method is suitable for deployment in real-time, streaming, or interactive 3D reconstruction applications, including robotics, AR/VR, and autonomous systems.
• Limitations: While TTT3R substantially improves length generalization, it does not fully close the gap to offline, full-attention models in terms of absolute reconstruction accuracy. The design space for confidence estimation and state update remains open for further exploration.

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Theoretical and Practical Implications

TTT3R demonstrates that test-time training principles, particularly confidence-guided fast weight updates, can be effectively leveraged to address the length generalization and forgetting problems in recurrent 3D reconstruction models. This approach bridges the gap between meta-learning, online adaptation, and large-scale 3D scene understanding, suggesting new directions for efficient, scalable sequence modeling in vision.

The framework also highlights the importance of internal confidence signals for robust online learning, a principle that may generalize to other domains such as LLMing and reinforcement learning.

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Conclusion

TTT3R provides a theoretically grounded, empirically validated solution to the catastrophic forgetting problem in online 3D reconstruction. By introducing a confidence-guided, training-free state update rule, it enables robust, scalable, and efficient processing of long video sequences, outperforming prior online methods and approaching the performance of resource-intensive offline models. Future work may extend this framework to more expressive confidence estimation, parallelizable recurrent architectures, and broader applications in sequential perception and reasoning.