Nvidia corporation (20240257443). SCENE RECONSTRUCTION FROM MONOCULAR VIDEO simplified abstract
Contents
SCENE RECONSTRUCTION FROM MONOCULAR VIDEO
Organization Name
Inventor(s)
Christopher B. Choy of Los Angeles CA (US)
Or Litany of Sunnyvale CA (US)
Charles Loop of Redmond WA (US)
Animashree Anandkumar of Pasadena CA (US)
Wei Dong of Pittsburgh PA (US)
SCENE RECONSTRUCTION FROM MONOCULAR VIDEO - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240257443 titled 'SCENE RECONSTRUCTION FROM MONOCULAR VIDEO
Simplified Explanation: The patent application describes a technique for reconstructing a three-dimensional scene from monocular video by adaptively allocating a sparse-dense voxel grid with dense voxel blocks around surfaces and sparse voxel blocks further from the surfaces. This two-level voxel grid allows for efficient querying and sampling, with the scene surface geometry represented as a signed distance field (SDF) that can be extended to multi-modal data. The properties stored in the sparse-dense voxel grid structure are differentiable, enabling optimization of the scene surface geometry through differentiable volume rendering.
Key Features and Innovation:
- Adaptive allocation of a sparse-dense voxel grid for reconstructing 3D scenes from monocular video.
- Representation of scene surface geometry as a signed distance field (SDF) that can be extended to multi-modal data.
- Differentiable properties stored in the voxel grid structure for optimizing scene surface geometry through differentiable volume rendering.
Potential Applications: This technology can be applied in various fields such as:
- Augmented reality
- Virtual reality
- Robotics
- Autonomous driving
- Medical imaging
Problems Solved:
- Efficient reconstruction of 3D scenes from monocular video
- Optimization of scene surface geometry through differentiable volume rendering
- Representation of multi-modal data in scene reconstruction
Benefits:
- Accurate reconstruction of 3D scenes
- Enhanced optimization of scene surface geometry
- Integration of multi-modal data for comprehensive scene representation
Commercial Applications: Potential commercial uses include:
- Development of advanced AR/VR applications
- Integration into robotics and autonomous systems
- Medical imaging software development
Prior Art: Readers can explore prior art related to this technology in the fields of computer vision, 3D reconstruction, and volumetric rendering techniques.
Frequently Updated Research: Stay updated on the latest advancements in computer vision, 3D reconstruction, and volumetric rendering techniques to enhance the application of this technology.
Questions about 3D Scene Reconstruction: 1. How does the adaptive allocation of a sparse-dense voxel grid improve the efficiency of reconstructing 3D scenes? 2. What are the potential challenges in optimizing scene surface geometry through differentiable volume rendering?
Original Abstract Submitted
a technique for reconstructing a three-dimensional scene from monocular video adaptively allocates an explicit sparse-dense voxel grid with dense voxel blocks around surfaces in the scene and sparse voxel blocks further from the surfaces. in contrast to conventional systems, the two-level voxel grid can be efficiently queried and sampled. in an embodiment, the scene surface geometry is represented as a signed distance field (sdf). representation of the scene surface geometry can be extended to multi-modal data such as semantic labels and color. because properties stored in the sparse-dense voxel grid structure are differentiable, the scene surface geometry can be optimized via differentiable volume rendering.
