Visual Geometry Grounded Transformer
PulseAugur coverage of Visual Geometry Grounded Transformer — every cluster mentioning Visual Geometry Grounded Transformer across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
-
EventVGGT framework enhances depth estimation using cross-modal distillation
Researchers have developed EventVGGT, a novel framework for event-based monocular depth estimation that addresses the scarcity of dense depth annotations. This approach leverages cross-modal distillation from Vision Fou…
-
RegimeVGGT accelerates 3D scene reconstruction with layer-wise compression
Researchers have developed RegimeVGGT, a method to improve the efficiency of Visual Geometry Grounded Transformers (VGGTs) for 3D scene reconstruction. Unlike previous methods that applied uniform computation reduction,…
-
RegimeVGGT accelerates 3D scene reconstruction with layer-wise compression
Researchers have developed RegimeVGGT, a novel method to accelerate the Visual Geometry Grounded Transformer (VGGT) for 3D scene reconstruction. By analyzing the layer-specific computational needs, RegimeVGGT applies ta…
-
New MVM-IOD Dataset Evaluates 3D Reconstruction in Industrial Settings
Researchers have introduced the Machine Vision Metrology Industrial Object Dataset (MVM-IOD), a new benchmark designed to evaluate 3D reconstruction and camera pose estimation methods in industrial settings. The dataset…
-
New FGQ method slashes Visual Geometry Transformer model size
Researchers have developed a new post-training quantization method called Fisher-Guided Quantization (FGQ) to reduce the memory and computation overhead of Visual Geometry Grounded Transformers (VGGT). These models, use…
-
Beyond Gaussian Bottlenecks: Topologically Aligned Encoding of Vision-Transformer Feature Spaces
Researchers have developed a new latent learning framework called S$^2$VAE designed to improve the representation of 3D geometry and camera dynamics in visual world models. This approach utilizes a geometry-first perspe…