arXiv Computer Vision
Multi-View Foundation Models
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Oct 15
Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective
arXiv:2604.14025v2 Announce Type: replace-cross Abstract: Reconstructing 3D representations from 2D inputs is a fundamental task in computer vision and graphics, serving as a cornerstone for understa...
arXiv Computer Vision
Sep 4
Zero-Shot Novel Depth Synthesis Using 3D Foundation Models Scene Representations
The paper introduces Z3D, a method that leverages internal representations from 3D Foundation Models (3DFMs) to perform zero‑shot novel depth synthesis. By decoding hidden surfaces and applying latent diffusion on 3DFM representations, Z3D can estimate realistic depth maps for unseen views across multiple datasets. This demonstrates that 3DFMs capture extensive general knowledge about 3D scenes, enabling accurate reconstruction without additional training.
arXiv Computer Vision
Aug 26
DDMS: Discriminative Distillation of Multi-view Foundational Features into Single-view Models
arXiv:2608.23850v1 Announce Type: new Abstract: Foundational visual features such as DINO have played a critical role across modern computer vision, and have recently become key components in multi-v...
arXiv AI
Oct 26
Easy-3Dfy: Object-Level Geometric Composition for Single-Image 3D Scene Construction
Easy-3Dfy introduces a compositional workflow for single-image 3D scene construction that separates object generation from scene layout estimation. By using a specialized 3D object generator for detailed geometry and a 3D foundation model for spatial understanding, the method aligns object-local meshes with partial scene pointmaps via an RPF-based aligner. This approach consistently outperforms SAM-3Dfy on geometric metrics in Scene-Maker and 3D-FUTURE200 without requiring full scene-level supervision.
arXiv Computer Vision
Sep 2
Monocular Depth Estimation from a Single Image: Progress and Opportunities
arXiv:2609.01172v1 Announce Type: new Abstract: Monocular depth estimation has long stood as a fundamental challenge in computer vision, enabling a wide range of applications including 3D reconstruct...
arXiv Computer Vision
Sep 3
Geometry-Guided Modeling of Foundation Features Enables Generalizable Object Shape Deformation Learning
The paper introduces a generalizable deformation learning framework that reconstructs 3D objects by deforming a category-level shape template to match a monocular observation. It employs a geometry-guided feature modeling mechanism to enrich foundation features with template topology, creating a geometry-aware representation that is explicitly correlated with the target observation for precise deformation. A view-adaptive feature aggregation module further bridges the gap between the fixed template and arbitrary target views by leveraging multi-view template features and camera poses, ensuring robust feature alignment across diverse viewpoints.