3d-vision

Presents Self-Geometry, a plug-and-play test-time adaptation pipeline that enforces explicit multi-view geometric constraints using 2D pixel correspondences to improve geometrically consistent 3D vision foundation models.

3DZip is a three-stage token compression framework for 3D vision-language models that uses voxelization, diversity-guided anchor selection, and spatial constraint merging to reduce tokens while preserving spatial reasoning. It retains 94.7% of original performance with only 128 tokens and achieves 1.92x faster inference on 3DQA benchmarks.

MetaView proposes a diffusion-based monocular novel view synthesis framework that combines implicit geometry priors with metric depth guidance to achieve consistent and controllable rendering under large viewpoint changes from a single image.

This paper presents a comprehensive taxonomy of 3D vision research, covering geometric representations, datasets, learning paradigms, and applications in reconstruction, generation, and video modeling.

This paper introduces a post-training framework that leverages 3D priors from SAM3D to improve semantic correspondence in 2D foundation features, addressing issues like left-right confusion and repeated parts. The method uses instance-specific 3D reconstruction without pose annotations or spherical geometry shortcuts.

SpatialBench is a comprehensive benchmark for evaluating spatial foundation models across diverse domains and tasks, revealing limitations in current models and introducing DA-Next-5M and DA-Next to advance spatial representation learning.

A Zhejiang University researcher shared a comprehensive PhD guide on GitHub, covering the entire research lifecycle from topic selection to rebuttals, specifically tailored for the 3D Vision direction.

Meta AI and Oxford VGG released VGGT-Omega, a foundation model for 3D vision, with project page and GitHub repository.