Streaming 3D reconstruction relies on a compact recurrent scene state to process long image streams in linear time and bounded memory. However, repeated updates can gradually corrupt this state, causing reliable historical information to be overwritten by noisy or ambiguous observations.
arXiv:2605.16981v3 Announce Type: replace Abstract: Streaming 3D reconstruction under a strict constant-memory budget hinges on how the recurrent state is updated as the stream evolves. We profile TT...
arXiv:2608.27529v1 Announce Type: new Abstract: Streaming 3D reconstruction from extremely long videos requires estimating camera motion and scene geometry online under bounded memory and computation...
arXiv:2609.21938v1 Announce Type: new Abstract: Transformer-based models have recently achieved strong performance on 3D reconstruction from images, and recent works extend them to process video stre...
FILT3R is a training‑free latent filtering layer for streaming 3D reconstruction that treats recurrent state updates as stochastic state estimation in token space. It maintains per‑token variance and computes a Kalman‑style gain to balance memory retention with new observations, estimating process noise online from temporal drift of candidate tokens. Experiments show that FILT3R generalizes overwrite and gating policies, shrinking gains in stable regimes and increasing them during genuine scene changes, thereby improving long‑horizon stability for depth, pose, and 3D reconstruction.
LoG-VGGT is a memory‑efficient framework for long‑sequence 3D reconstruction that balances local temporal modeling with global camera consistency. It uses cross‑window attention in a small subset of transformer blocks to propagate information across adjacent temporal windows while keeping memory usage bounded. A global camera consistency refinement module further improves long‑horizon pose stability by enforcing scene‑level constraints through cross‑attention between camera and compact register tokens, leading to better depth accuracy and robust camera pose estimation on multiple benchmarks.
Anchor3R is a streaming 3D reconstruction framework that predicts window-relative poses and local geometry in the current‑frame coordinate system, forming a dense relative‑pose graph for online pose updates and loop‑aware motion averaging. It improves long‑horizon pose accuracy and dense reconstruction quality on indoor, outdoor, driving, and RGB‑D benchmarks, and generalizes from 48‑frame training sequences to streams exceeding 10,000 frames while keeping GPU memory bounded. The method addresses issues of train‑test mismatch, early‑anchor bias, and accumulated drift found in previous streaming models.
Forge4D is a feed‑forward model that reconstructs temporally aligned 4D human representations from uncalibrated sparse‑view videos, enabling both novel view and novel time synthesis. It achieves this by jointly streaming 3D Gaussian reconstruction with dense motion prediction, using learnable state tokens for temporal consistency and a self‑supervised retargeting loss for motion prediction. Extensive experiments confirm its effectiveness on in‑domain and out‑of‑domain datasets.
Scal3R is a new method for online 3D reconstruction that addresses the failure of traditional models on long videos by decoupling per‑frame depth from global pose estimation. It reformulates reconstruction as a multi‑reference relative pose query, using lightweight learnable tokens (~1% of parameters) injected into a frozen backbone via asymmetric attention to query poses relative to multiple past keyframes. An online pose‑graph optimization with loop closure further suppresses drift, achieving convergence in 8 hours on a single GPU and reducing average absolute trajectory error by over 60% on KITTI while setting state‑of‑the‑art results on several benchmark datasets.
arXiv:2609.00923v1 Announce Type: new Abstract: While feed-forward 3D reconstruction (3R) offers efficient end-to-end modeling, its application in large-scale UAV mapping is hindered by the prohibiti...
The paper introduces a reliability-regulated trajectory optimization framework for progressive COLMAP‑free 3D Gaussian Splatting (3DGS). It uses a self‑supervised bidirectional cycle‑consistency mechanism to control camera trajectory estimation through forward motion propagation and retrospective trajectory correction, thereby reducing error compounding without external priors. Experiments on Tanks and Temples and CO3D‑V2 demonstrate improved camera trajectory accuracy and novel‑view rendering quality compared to existing unposed baselines.
arXiv:2609.23286v1 Announce Type: new Abstract: 3D reconstruction from a lengthy video stream input poses a dilemma for feed-forward reconstruction models (FFRMs), that a whole-stream inference conte...
arXiv:2609.17230v1 Announce Type: new Abstract: Streaming 3D reconstruction demands both speed and temporal fidelity, goals that existing methods undermine by updating every Gaussian every frame, eve...