ObjectSplat: Improving Mesh Fidelity and Interactivity for 3D Scenes via Object-Level Mesh Splatting
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arXiv:2608.20687v1 Announce Type: new Abstract: 3D Gaussian Splatting has achieved remarkable success in novel view synthesis. However, extracting high-fidelity surfaces directly from 3DGS remains ch...
SpatialCrafter introduces a two‑stage framework for single‑image world modeling that first generates a global 3D proxy using a Point‑anchored Sparse Structure Flow module, then refines appearance with a Generative Deferred Refiner built on a video diffusion model. The method incorporates Parallel Geometry Injection and Proxy‑Aware Corruption training to integrate the proxy without disrupting the pretrained generative manifold, and it is evaluated on a newly constructed dataset of 115K scenes. Experiments demonstrate that SpatialCrafter outperforms existing approaches, reducing long‑term drift and maintaining consistency under rapid camera motion and extreme viewpoints.
The paper introduces MILO, a framework that uses Large Reconstruction Models (LRMs) to reconstruct detailed 3D human‑object interactions from a single image. By treating the LRM mesh as a geometric scaffold, MILO segments it into human and object parts, fits a parametric body model to the human component, and optionally aligns an object template to the object component. The approach achieves higher reconstruction accuracy than existing baselines across multiple benchmarks and interaction scenarios.
SceneReGen is a new framework for reconstructing 3D scenes from a single image by generating and assembling complete object meshes within a shared observation‑aligned scene frame. It uses selective pose factorization to encode each object’s observed orientation directly into the generated mesh, while estimating translation and scale from instance‑level and global scene cues. Evaluated on the 3D‑FUTURE dataset, SceneReGen outperforms existing methods on scene‑level metrics and shows strong performance on object‑level metrics, demonstrating its effectiveness in autonomous‑driving and embodied‑AI scenarios.
arXiv:2603. 16085v2 Announce Type: replace-cross Abstract: Recent breakthroughs in 3D generation have enabled the synthesis of high-fidelity individual assets.
arXiv:2604. 05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.