TTSA3R: Training-Free Temporal-Spatial Adaptive Persistent State for Streaming 3D Reconstruction

• URL: http://arxiv.org/abs/2601.22615v1
• Date: Fri, 30 Jan 2026 06:14:42 GMT
• Title: TTSA3R: Training-Free Temporal-Spatial Adaptive Persistent State for Streaming 3D Reconstruction
• Abstract summary: Streaming recurrent models enable efficient 3D reconstruction by maintaining persistent state representations.<n>Recent methods alleviate this by deriving adaptive signals from attention perspective.<n>We propose a training-free framework termed TTSA3R that leverages both temporal state evolution and spatial observation quality.
• Score: 11.124244013253806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Streaming recurrent models enable efficient 3D reconstruction by maintaining persistent state representations. However, they suffer from catastrophic memory forgetting over long sequences due to balancing historical information with new observations. Recent methods alleviate this by deriving adaptive signals from attention perspective, but they operate on single dimensions without considering temporal and spatial consistency. To this end, we propose a training-free framework termed TTSA3R that leverages both temporal state evolution and spatial observation quality for adaptive state updates in 3D reconstruction. In particular, we devise a Temporal Adaptive Update Module that regulates update magnitude by analyzing temporal state evolution patterns. Then, a Spatial Contextual Update Module is introduced to localize spatial regions that require updates through observation-state alignment and scene dynamics. These complementary signals are finally fused to determine the state updating strategies. Extensive experiments demonstrate the effectiveness of TTSA3R in diverse 3D tasks. Moreover, our method exhibits only 15% error increase compared to over 200% degradation in baseline models on extended sequences, significantly improving long-term reconstruction stability. Our codes will be available soon.

Related papers

• Towards Physically Consistent 4D Scene Reconstruction for Closed-loop Autonomous Driving Simulation [19.055975606481635]
  High-fidelity street scene reconstruction is pivotal for end-to-end autonomous driving simulation.<n>Existing 3DGS methods and their 4D extensions fail to simultaneously achieve both.<n>We propose Orthogonal Projected Gradient (OPG) to restore spatial identifiability.
  arXiv  Detail & Related papers  (2026-05-20T11:09:53Z)
• Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction [50.5449251266956]
  This paper addresses the task of large-scale 3D scene reconstruction from long video sequences.<n>Recent feed-forward reconstruction models have shown promising results by directly regressing 3D geometry from RGB images without explicit 3D priors or geometric constraints.<n>We propose a novel neural global context representation that efficiently compresses and retains long-range scene information.
  arXiv  Detail & Related papers  (2026-04-09T17:59:50Z)
• OSA: Echocardiography Video Segmentation via Orthogonalized State Update and Anatomical Prior-aware Feature Enhancement [33.432715703434475]
  We introduce OSA, a framework that constrains state evolution on the Stiefel manifold.<n>We show that OSA achieves state-of-the-art segmentation accuracy and temporal stability, while maintaining real-time inference efficiency for clinical deployment.
  arXiv  Detail & Related papers  (2026-03-27T09:02:16Z)
• FILT3R: Latent State Adaptive Kalman Filter for Streaming 3D Reconstruction [51.56484100374058]
  Streaming 3D reconstruction maintains a persistent latent state that is updated online from incoming frames.<n>FILT3R casts recurrent state updates as state estimation in token space.<n>Code will be released at https://github.com/jinotter3/FILT3R.
  arXiv  Detail & Related papers  (2026-03-19T04:56:36Z)
• SSR: A Training-Free Approach for Streaming 3D Reconstruction [53.19807901585702]
  Self-expressive Sequence Regularization (SSR) is a plug-and-play operator that enforces Grassmannian sequence regularity during inference.<n>We show that SSR consistently reduces drift and improves reconstruction quality across multiple streaming 3D reconstruction tasks.
  arXiv  Detail & Related papers  (2026-03-16T02:55:14Z)
• OnlineX: Unified Online 3D Reconstruction and Understanding with Active-to-Stable State Evolution [34.8105632078785]
  We introduce OnlineX, a feed-forward framework that reconstructs both 3D visual appearance and language fields in an online manner using only streaming images.<n>Our framework decouples the memory state into a dedicated active state and a persistent stable state, and then cohesively fuses the information from the former into the latter to achieve both fidelity and stability.
  arXiv  Detail & Related papers  (2026-03-02T17:52:02Z)
• Long-Term Multi-Session 3D Reconstruction Under Substantial Appearance Change [52.46888249268445]
  Long-term environmental monitoring requires the ability to reconstruct and align 3D models across repeated site visits separated by months or years.<n>Existing approaches rely on post-hoc alignment of independently reconstructed sessions.<n>We propose enforcing cross-session correspondences directly within a joint SfM reconstruction.
  arXiv  Detail & Related papers  (2026-02-24T06:12:51Z)
• Learning Dynamic Scene Reconstruction with Sinusoidal Geometric Priors [8.153339288887922]
  We propose SirenPose, a novel loss function that combines the periodic activation of sinusoidal representation networks with geometric priors derived from keypoint structures to improve the accuracy of 3D scene reconstruction.
  arXiv  Detail & Related papers  (2025-12-25T20:51:19Z)
• RecurGS: Interactive Scene Modeling via Discrete-State Recurrent Gaussian Fusion [21.761449995572757]
  RecurGS is a recurrent fusion framework that integrates discrete Gaussian scene states into a single evolving representation.<n>A voxelized, visibility-aware fusion module selectively incorporates newly observed regions while keeping stable areas fixed.<n>Our framework delivers high-quality reconstructions with substantially improved update efficiency.
  arXiv  Detail & Related papers  (2025-12-20T14:53:22Z)
• StarPose: 3D Human Pose Estimation via Spatial-Temporal Autoregressive Diffusion [29.682018018059043]
  StarPose is an autoregressive diffusion framework for 3D human pose estimation.<n>It incorporates historical 3D pose predictions and spatial-temporal physical guidance.<n>It achieves superior accuracy and temporal consistency in 3D human pose estimation.
  arXiv  Detail & Related papers  (2025-08-04T04:50:05Z)
• STCOcc: Sparse Spatial-Temporal Cascade Renovation for 3D Occupancy and Scene Flow Prediction [2.884410617643992]
  3D occupancy and scene flow offer a detailed and dynamic representation of 3D scene.<n>Previous vision-centric methods have employed implicit learning-based approaches to model spatial and temporal information.<n>We propose a novel explicit state-based modeling method designed to leverage the occupied state to renovate the 3D features.
  arXiv  Detail & Related papers  (2025-04-28T12:49:20Z)
• SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data Pretraining [62.433137130087445]
  SuperFlow++ is a novel framework that integrates pretraining and downstream tasks using consecutive camera pairs.<n>We show that SuperFlow++ outperforms state-of-the-art methods across diverse tasks and driving conditions.<n>With strong generalizability and computational efficiency, SuperFlow++ establishes a new benchmark for data-efficient LiDAR-based perception in autonomous driving.
  arXiv  Detail & Related papers  (2025-03-25T17:59:57Z)
• ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions [91.55655961014027]
  3D semantic occupancy and flow prediction are fundamental to understanding scene scene.<n>This paper proposes a vision-based framework with three targeted improvements.<n>Our purely convolutional architecture establishes new SOTA performance on multiple benchmarks for both semantic occupancy and joint semantic-flow prediction.
  arXiv  Detail & Related papers  (2024-11-12T11:32:56Z)
• Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change [82.31647863785923]
  Building 3D geometric maps of man-made spaces are fundamental computer vision and robotics.<n>Nothing Stands Still (NSS) benchmark focuses on thetemporal registration of 3D scenes undergoing large spatial and temporal change.<n>As part of NSS, we introduce a dataset of 3D point clouds recurrently captured in large-scale building indoor environments that are under construction or renovation.
  arXiv  Detail & Related papers  (2023-11-15T20:09:29Z)
• Multi-Temporal Convolutions for Human Action Recognition in Videos [83.43682368129072]
  We present a novel temporal-temporal convolution block that is capable of extracting at multiple resolutions. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture.
  arXiv  Detail & Related papers  (2020-11-08T10:40:26Z)
• SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion [86.77318031029404]
  We propose a framework that performs scene reconstruction and semantic scene completion jointly in an incremental and real-time manner. Our framework relies on a novel neural architecture designed to process occupancy maps and leverages voxel states to accurately and efficiently fuse semantic completion with the 3D global model.
  arXiv  Detail & Related papers  (2020-10-26T15:31:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.