LVSPM: Long Sequence View Synthesis and Pose Estimation Model

arXiv:2610.10960 · cs.CV · Submitted 2026-10-07 · Read on arXiv

cs.CV

Submitted: 2026-10-07

Updated: 2026-10-07

Project page: https://burningdust21.github.io/Projects/LVSPM

Terminology

• Conference on Computer Vision and Pattern Recognition — 12× in this paper
• Gaussian Splatting — 12× in this paper · explained in 4 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Accuracy — 6× in this paper · explained in 1 episode
• 3D Gaussian Splatting — 5× in this paper · explained in 2 episodes
• ACM Transactions on Graphics — 5× in this paper
• AUC — 4× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Area Under Curve — 1× in this paper
• Aria Synthetic Environments — 1× in this paper
• Calibration — 1× in this paper · explained in 7 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Digital Twin — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feed-forward framework — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• TTT3R: 3D Reconstruction as Test-Time Training
• Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
• InstantSplat: Sparse-view Gaussian Splatting in Seconds
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• No Pose at All: Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views
• LEAP: Liberate Sparse-view 3D Modeling from Camera Poses
• RayZer: A Self-supervised Large View Synthesis Model
• AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
• Few-View Object Reconstruction with Unknown Categories and Camera Poses
• RelPose++: Recovering 6D Poses from Sparse-view Observations
• Depth Anything 3: Recovering the Visual Space from Any Views
• MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo
• R2D2: Repeatable and Reliable Detector and Descriptor
• GLU Variants Improve Transformer
• Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs
• Learning to (Learn at Test Time): RNNs with Expressive Hidden States
• MeshLRM: Large Reconstruction Model for High-Quality Meshes
• SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction
• FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction
• No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images

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