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...
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:2610.35066v1 Announce Type: new Abstract: Vision-language navigation (VLN) requires an embodied agent to follow language instructions through 3D environments. Maintaining persistent episodic me...
arXiv:2610.17434v1 Announce Type: cross Abstract: Recent advances in video world models have enabled plausible interactive environments, yet long-horizon generation places a fundamental demand on vis...
The paper introduces Adaptive World Memory 3D Foundation Model (AWM-3DFM), a memory‑centric 3D foundation model that scales to large‑scale robotic localization, reconstruction, and Gaussian rendering. It employs transformer‑based gated updates, test‑time temporal‑spatial regulation, and local submap organization to maintain persistent memory, accuracy, and consistency across long image sequences. A Gaussian reconstruction head unifies pose estimation, dense point‑cloud reconstruction, and photorealistic rendering, achieving superior trajectory accuracy, reconstruction completeness, and rendering quality on public benchmarks and diverse robotic datasets.
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...
Slot3R introduces a set‑associative spatial memory that allows multiple states to coexist at the same 3D location, addressing the limitation of Point3R where spatial proximity forces merging of distinct observations. By keeping the pretrained Point3R backbone frozen and adding a bounded sparse readout, Slot3R improves point‑cloud accuracy by up to 72% on benchmark datasets and reduces trajectory error across pose benchmarks, while maintaining real‑time performance up to 1000 frames without exhausting memory.
DiffWAM is a geometry‑conditioned navigation world‑action model that transforms predictive features from a frozen video foundation model into continuous camera trajectories, eliminating the need for future‑video synthesis and multi‑frame reconstruction during deployment. Its Grid‑Motion module preserves spatial‑temporal motion associations, while Latent2Pose grounds them with first‑frame geometry to recover metrically meaningful 3D motion. The system, complemented by FastDreamer for asynchronous trajectory handoff, achieves a trajectory RMSE of 0.3492 m and a 74.40 % endpoint success rate on the DiffWAM‑1000 benchmark, with real‑world tests showing complex UAV behaviors and an onboard implementation reaching 1.08 s latency on NVIDIA Jetson AGX Thor.
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.
arXiv:2605.23889v2 Announce Type: replace Abstract: Online 3D reconstruction requires estimating camera pose and scene geometry under strict causal and bounded-memory constraints. Existing methods of...
WorldCrafter is a video world model that introduces a camera‑queryable implicit 3D‑aware memory to improve long‑horizon consistency and viewpoint control. The model compresses multi‑view evidence into a limited token budget shaped by the requested viewpoint, integrating historical observations via a memory encoder and pose‑conditioned readout before denoising. Experiments on static and dynamic scenes demonstrate significant gains in consistency and camera‑control accuracy while maintaining visual quality during minute‑scale exploration.
arXiv:2610.29866v1 Announce Type: new Abstract: Geometric structure is essential for robotic manipulation and understanding the 3D world. Most VLA models rely on 2D representations of current observa...