arXiv AI
Jun 4
DVGT: Driving Visual Geometry Transformer
arXiv:2512. 16919v2 Announce Type: replace-cross Abstract: Perceiving and reconstructing 3D scene geometry from visual inputs is crucial for autonomous driving.
Motion- and Gravity-Aware Feedforward 3D Reconstruction
The paper presents a motion- and gravity-aware framework for feed-forward transformer models in multi-view 3D reconstruction. By providing residual optical flow and gravity direction as privileged signals during training, the model learns stronger geometric representations while remaining efficient at inference. Experiments demonstrate consistent accuracy gains, particularly in dynamic scenes and challenging camera motions, with the optimal integration stage depending on the backbone architecture.
Related stories
arXiv Computer Vision
Sep 22
Revisiting Multi-View Stereo: A Sequence-to-Sequence Formulation
The paper proposes a new sequence-to-sequence formulation for multi-view stereo (MVS) that jointly predicts 3D geometry for all input views using a global transformer architecture. It introduces ray‑map embeddings to inject camera parameters into image tokens and a unified global cost volume to capture 3D structure across all views. Experiments on public benchmarks demonstrate state‑of‑the‑art performance, outperforming both traditional MVS and feed‑forward reconstruction baselines.
arXiv AI
4d ago
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
4d ago
TurboVGGT: Fast Visual Geometry Reconstruction with Adaptive Alternating Attention
arXiv:2605.14315v2 Announce Type: replace Abstract: Recent feed-forward 3D reconstruction methods, such as visual geometry transformers, have substantially advanced the traditional per-scene optimiza...
Hugging Face Trending Papers
Jul 21
Latent Riemannian Flow Matching for Geometry-Grounded 3D Foundation Models
Geometric foundation models, such as the Visual Geometry Grounded Transformer (VGGT), provide strong 3D priors from unposed images. However, such models operate purely in a feed-forward, deterministic regime, \ie~they cannot generate plausible geometry beyond what the input views directly support.
arXiv Computer Vision
3d ago
ControlFlow3D: Distilling Multi-View Knowledge into Latent Flow Matching for Point Cloud Upsampling
ControlFlow3D is a framework that upsamples point clouds by distilling knowledge from pretrained foundation models into a lightweight generation network. It performs conditional flow matching in a latent space defined by a frozen 3D encoder, using unsupervised soft KMeans to cluster multi‑view image tokens and 3D latent tokens into shared prototypes that guide the flow trajectory. A Mamba‑based geometry decoder then maps latent features to dense coordinates with linear complexity, and the authors introduce ShapeNetPU, a 35K‑object multi‑modal benchmark 31 times larger than previous datasets.
arXiv AI
Aug 25
Learning Implicit Constitutive Laws for Dynamic 3D Gaussian Splatting from Monocular Videos
arXiv:2608.22102v1 Announce Type: cross Abstract: We present GCA (Gaussian Constitutive Alignment), a framework for learning implicit constitutive laws from monocular dynamic video of deformable obje...
arXiv AI
Sep 18
FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations
FAMOS is a feed‑forward model that predicts movable‑part segmentation and joint parameters from a sparse, unordered set of partial point clouds. It jointly reasons over multiple observations using a Multi‑state Articulation Transformer that alternates state‑wise and global attention, and introduces an observed articulation span objective to supervise motion ranges across inputs. A procedural data generator supplies self‑annotated assets for training, and experiments on PartNet‑Mobility, ACD, and ArtiCraft‑10K show consistent improvements over existing feed‑forward and optimization‑based baselines.
arXiv Computer Vision
Sep 22
VGGT-Prime: Compute-Adaptive Mixture-of-Heads for Efficient Visual Geometry Transformers
arXiv:2609.23733v1 Announce Type: new Abstract: Feed-forward visual geometry models such as the Visual Geometry Grounded Transformer (VGGT) have recently enabled direct 3D reconstruction from multi-v...
arXiv Computer Vision
Sep 10
OmniPoint: Universal Monocular Metric Pointcloud from Any Camera
arXiv:2609.09394v1 Announce Type: new Abstract: Recovering metric 3D geometry from monocular images is a fundamental computer vision task, yet current methods remain heavily fragmented by fixed camer...
arXiv Computer Vision
Oct 9
GenIA: Generative Reconstruction with Test-Time Input Alignment
GenIA is a framework that aligns generative 3D foundation models with test‑time observations, improving pose estimation and reconstruction from monocular, multi‑view, and dynamic inputs. It derives translation and scale from geometry, retains learned rotation priors, and aligns appearance using visibility‑biased attention, cross‑observation fusion, and differentiable rendering guidance during denoising. An optional post‑denoising refinement further adapts appearance latents and object placement, and the method also supports externally supplied geometry for dynamic objects, achieving better results than recent optimization‑based and image‑to‑3D methods on synthetic and real benchmarks.
Hugging Face Trending Papers
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.