GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.
Yonosplat: You only need one model for feedforward 3d gaussian splatting
7 Pith papers cite this work. Polarity classification is still indexing.
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NoPo4D is the first feed-forward system for dynamic 4D Gaussian splatting from unposed multi-view videos, using velocity decomposition supervised by optical flow and a bidirectional motion encoder.
ConFixGS repairs feedforward 3D Gaussian Splatting with confidence-aware diffusion priors, delivering up to 3.68 dB PSNR gains and halved FID scores on Waymo, nuScenes, and KITTI novel view synthesis tasks.
GlobalSplat achieves competitive novel-view synthesis on RealEstate10K and ACID using only 16K Gaussians via global scene tokens and coarse-to-fine training, with a 4MB footprint and under 78ms inference.
AnchorSplat uses anchor-aligned 3D Gaussians guided by geometric priors for feed-forward scene reconstruction, achieving SOTA novel view synthesis on ScanNet++ with fewer primitives and better view consistency.
The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temporal-aware modeling.
VVGT is a dual-transformer network with Volume Geometry Forcing that maps volumetric data to 3D Gaussian primitives for accurate ray-based rendering without per-scene optimization.
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VVGT: Visual Volume-Grounded Transformer
VVGT is a dual-transformer network with Volume Geometry Forcing that maps volumetric data to 3D Gaussian primitives for accurate ray-based rendering without per-scene optimization.