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TableVerse introduces a fully automated Real2Sim pipeline that converts unstructured, in-the-wild images into high-fidelity, simulation-ready tabletop environments with accurate metrics and physical stability, along with a large-scale dataset (TableVerse-100K) for generalizable robotic manipulation.
ATSplat introduces a feed-forward 3D Gaussian Splatting framework that uses adaptive 3D tokens to allocate primitives based on scene complexity, achieving state-of-the-art rendering quality while reducing the number of Gaussians by over 5.7 times compared to dense methods.
WildCity introduces a large-scale multimodal dataset for city-scale urban navigation and spatial representation, collected by autonomous fleets. It provides 18 long trajectories and establishes baselines for reconstruction and closed-loop simulation to advance AI systems that can perceive and reason about city-scale environments.
ZipSplat is a token-based feed-forward 3D Gaussian Splatting model that uses k-means clustering to decouple Gaussian placement from the pixel grid, achieving ~6x fewer Gaussians while setting new state-of-the-art results on DL3DV and RealEstate10K without requiring ground-truth poses or intrinsics.
PropLLM integrates hop-by-hop scene reconstruction with LLMs for network fault diagnosis. It uses a dual-layer knowledge graph and a temporal causal propagation attention mechanism to trace back along propagation paths, improving accuracy and reducing hallucinations.
RayDer is a unified feed-forward transformer that consolidates camera estimation, scene reconstruction, and rendering for self-supervised novel view synthesis from real-world video, achieving clean power-law scaling and strong zero-shot performance.
GenRecon introduces a method for 3D scene reconstruction that integrates generative 3D priors with multi-view image conditioning, achieving high-fidelity, editable mesh reconstructions of indoor environments and outperforming existing methods by 16%.