StereoVGGT: A Training-Free Visual Geometry Transformer for Stereo Vision
Driven by the advancement of 3D devices, stereo vision tasks including stereo matching and stereo conversion have emerged as a critical research frontier. Contemporary stereo vision backbones typically rely on either Monocular Depth Estimation models or general-purpose Pre-trained Vision Models. Crucially, these models are predominantly pretrained without explicit supervision of camera poses. Given that such geometric knowledge is indispensable for stereo vision, the absence of explicit spatial constraints constitutes a significant performance bottleneck for existing architectures. Recognizing that the Visual Geometry Grounded Transformer (VGGT) operates as a foundation model pre-trained ...
| Authors | Ziyang Chen, Yansong Qu, You Shen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 31 March 2026 |
| Last updated at source | 4 August 2026 |
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