arXiv:2512.16919v2 Announce Type: replace-cross Abstract: Perceiving and reconstructing 3D scene geometry from visual inputs is crucial for autonomous driving. However, there still lacks a driving-targeted dense geometry perception model that can adapt to different scenarios and camera configurations. To bridge this gap, we propose a Driving Visual Geometry Transformer (DVGT), which reconstructs a global dense 3D point map from a sequence of unposed multi-view visual inputs. We first extract visual features for each image using a DINO backbone, and employ alternating intra-view local attention
The continuous advancements in computer vision and transformer architectures are enabling more sophisticated 3D environmental perception, crucial for autonomous systems.
This development is crucial for autonomous driving and robotics, directly addressing a critical limitation in current 3D scene understanding from diverse visual inputs.
The ability to reconstruct dense, global 3D point maps from unposed, multi-view visual inputs will improve the robustness and adaptability of autonomous navigation systems.
- · Autonomous vehicle manufacturers
- · Robotics companies
- · AI hardware developers
- · Mapping and surveying services
- · Companies relying on less robust 3D perception methods
- · Traditional sensor-heavy autonomous systems
- · Software providers with outdated geometry reconstruction techniques
Improved reliability and safety of autonomous vehicles and robots in complex environments.
Accelerated development and broader deployment of self-driving cars and advanced robotic systems in more diverse operational scenarios.
Enhanced efficiency and precision in logistics, manufacturing, and construction through highly accurate 3D spatial awareness.
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Read at arXiv cs.AI