arXiv Computer Vision
PRISM: Feed-Forward Single-Image 3D Reconstruction via Geometric Warp-Residual Modeling
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Jun 24
PRISM: Feed-Forward Single-Image 3D Reconstruction via Geometric Warp-Residual Modeling
Reconstructing 3D scenes from a single image is a fundamental challenge in computer vision, with broad applications in virtual reality, robotics, and content creation. Recent methods achieve outstanding performance by leveraging camera-controlled video diffusion models, but rely on iterative diffusion sampling, which greatly limits their practical deployment.
arXiv Computer Vision
Oct 9
ASV3D: Adapting Diffusion-Based Single-View 3D Reconstruction with Extra Imagery
ASV3D is a framework that adapts diffusion-based single-view 3D reconstruction to incorporate an additional image at test time. It offers a zero‑shot adaptation that improves reconstruction quality without retraining, and an optimized adaptation that uses contrastive learning to enhance visual fidelity and cross‑view consistency. The method is applied to two state‑of‑the‑art pipelines, showing consistent gains in accuracy and robustness on benchmark and real‑world datasets.
arXiv Computer Vision
Sep 1
ReconSplat: Generalizable 3D Scene Reconstruction Beyond Observed Views
arXiv:2608.28895v1 Announce Type: new Abstract: We introduce ReconSplat, a feed-forward model for 3D scene reconstruction that aims to address the longstanding trade-off between plausible view genera...
arXiv Computer Vision
Aug 31
Video Generative Models as Geometry Learner
The paper introduces GeoNeXt, a framework that repurposes pretrained video generative models for geometry estimation by framing it as a next‑frame prediction task. Unlike prior methods that either train separate depth/normal models or fine‑tune image diffusion backbones, GeoNeXt jointly models images and geometric targets, leveraging the structured knowledge of video models for more data‑efficient learning. Experiments show zero‑shot monocular depth and surface normal estimation that outperforms existing generative approaches and rivals discriminative state‑of‑the‑art methods while using far less training data.
arXiv Computer Vision
Sep 10
GTA: Advancing Image-to-3D World Generation via Geometry Then Appearance Video Diffusion
arXiv:2605.12957v2 Announce Type: replace Abstract: Recent developments in generative models and large-scale datasets have substantially advanced 3D world generation, facilitating a broad range of do...
arXiv Computer Vision
Sep 30
GeoVerse: World-Consistent Novel View Synthesis in Geometric Latent Space
arXiv:2609.35734v2 Announce Type: replace Abstract: Novel view synthesis from sparse images must reconcile faithful reconstruction of observed regions with plausible completion of unseen content, whi...