Adaptive World Memory 3D Foundation Model for Scalable 3D Mapping, Localization, and Rendering

arXiv:2609.21502 · cs.CV, cs.RO · Submitted 2026-09-18 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-18

Updated: 2026-09-18

Code: https://github.com/dtc111111/AWM-3DFM

Terminology

• Consistency — 30× in this paper · explained in 3 episodes
• Foundation Model — 30× in this paper · explained in 3 episodes
• Foundation Models — 20× in this paper · explained in 7 episodes
• Benchmark — 16× in this paper · explained in 2 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Alignment — 15× in this paper · explained in 1 episode
• Transformer — 13× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 8× in this paper
• Gaussian Splatting — 7× in this paper · explained in 4 episodes
• Decode — 6× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Point Clouds — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• 3D Gaussian Splatting — 3× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Temporal Reasoning — 2× in this paper · explained in 2 episodes
• World Model — 2× in this paper · explained in 7 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• KV cache — 1× in this paper · explained in 3 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• $\pi^3$: Permutation-Equivariant Visual Geometry Learning
• What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models
• Streaming 4D Visual Geometry Transformer
• InfiniteVGGT: Visual Geometry Grounded Transformer for Endless Streams
• 3D Reconstruction with Spatial Memory
• Continuous 3D Perception Model with Persistent State
• TTSA3R: Training-Free Temporal-Spatial Adaptive Persistent State for Streaming 3D Reconstruction
• GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
• Compact 3D Gaussian Splatting For Dense Visual SLAM
• AMB3R: Accurate Feed-forward Metric-scale 3D Reconstruction with Backend
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• Learning to (Learn at Test Time): RNNs with Expressive Hidden States
• GRS-SLAM3R: Real-Time Dense SLAM with Gated Recurrent State
• Mamba-VGGT: Persistent Long-Sequence Video Geometry Grounded Transformer via External Sliding Window Mamba Memory
• Test-Time Training Done Right
• ZipMap: Linear-Time Stateful 3D Reconstruction via Test-Time Training
• LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory
• Mem3R: Streaming 3D Reconstruction with Hybrid Memory via Test-Time Training
• VGG-T$^3$: Offline Feed-Forward 3D Reconstruction at Scale
• MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors

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