SpatialBench evaluates 41 spatial foundation models across 6 paradigms and 5 task suites, finds they are not all-round players, and introduces the DA-Next-5M dataset plus DA-Next baseline model.
VGGT-SLAM 2.0: Real-time dense feed- forward scene reconstruction,
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LEXI-SG is the first monocular RGB system for dense open-vocabulary 3D scene graphs that partitions scenes into rooms and performs feed-forward reconstruction per room before global factor-graph alignment.
LeanGate is a lightweight feed-forward network that predicts geometric utility scores to skip over 90% of redundant frames in GFM-based monocular SLAM, reducing tracking FLOPs by 85% and achieving 5x speedup while maintaining accuracy.
A monocular SLAM-plus-semantics pipeline builds a class-inflated ESDF that supplies distance and gradient inputs to a CBF controller for online safe teleoperation and navigation.
ScaRF-SLAM decouples classical visual SLAM tracking from GFM mapping and adds lightweight scale optimization across submaps to achieve higher trajectory accuracy and 10-20% better reconstruction precision on building-scale indoor and outdoor datasets.
The paper proposes ray-aware pointer memory with adaptive retain-or-replace updates to improve long-term stability and pose accuracy in streaming 3D reconstruction.
Mono-Hydra++ is a monocular RGB-IMU pipeline that constructs hierarchical 3D scene graphs in real time while reporting lower trajectory error than some RGB-D baselines on indoor datasets.
MR.ScaleMaster adds a false-loop alarm and per-session Sim(3) scale estimation to enable accurate multi-agent monocular mapping, showing 7.2x ATE improvement on KITTI with up to 15 agents.
MonoEM-GS stabilizes view-dependent geometry from foundation models inside a global Gaussian Splatting representation via EM and adds multi-modal features for in-place open-set segmentation.
citing papers explorer
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SpatialBench: Is Your Spatial Foundation Model an All-Round Player?
SpatialBench evaluates 41 spatial foundation models across 6 paradigms and 5 task suites, finds they are not all-round players, and introduces the DA-Next-5M dataset plus DA-Next baseline model.
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Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring
LeanGate is a lightweight feed-forward network that predicts geometric utility scores to skip over 90% of redundant frames in GFM-based monocular SLAM, reducing tracking FLOPs by 85% and achieving 5x speedup while maintaining accuracy.
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Ray-Aware Pointer Memory with Adaptive Updates for Streaming 3D Reconstruction
The paper proposes ray-aware pointer memory with adaptive retain-or-replace updates to improve long-term stability and pose accuracy in streaming 3D reconstruction.