Monocular 3D Scene Reconstruction With Sparse-Dense Voxel Grids
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Solution Overview
Problem
Conventional scene reconstruction techniques from monocular video are slow or limited to reconstructing single objects, relying on implicit neural networks that are slow to train, and lack efficient methods for reconstructing entire scenes with high accuracy and speed.
Innovation Solution
A globally sparse and locally dense voxel grid structure is used for scene reconstruction, employing a signed distance field representation and differentiable volume rendering, with sparse voxel blocks around surfaces and dense voxels for fast query and sampling, and incorporating semantic priors for geometric refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If implicit neural networks are used for scene reconstruction, then the reconstruction can be performed, but the training speed is slow
Solution Approach 1:
The patent segments the continuous 3D space into discrete voxel grids with different resolutions. The scene is divided into multiple octrees or hierarchical voxel levels, where coarse grids cover large areas and fine grids focus on detailed regions. This segmentation allows the system to avoid processing the entire scene at high resolution, significantly reducing training complexity while maintaining reconstruction quality in important areas.
Solution Approach 2:
The patent applies local quality by using variable-resolution voxel grids where different regions of the scene have different levels of detail. Important regions (e.g., foreground objects, areas with motion) use fine-grained voxel grids, while background or static regions use coarse grids. This localized refinement reduces overall computational complexity while maintaining high reconstruction quality where needed.
2Productivity
If fast reconstruction techniques are used, then the processing speed is improved, but the reconstruction is limited to single objects or entire scenes with reduced accuracy
Solution Approach 1:
The patent introduces a hierarchical dimension by organizing voxels in multiple levels of resolution (e.g., octree structure). Instead of using a single uniform grid, the system creates a multi-dimensional hierarchy where Level 0 is coarse and Level N is fine. This dimensional organization allows fast processing at coarse levels and high-precision reconstruction at fine levels, resolving the speed-accuracy tradeoff.
Solution Approach 2:
The patent employs dynamic voxel grid allocation where the resolution and presence of voxels are adjusted based on scene content and importance. The system dynamically refines voxels in regions requiring high accuracy while coarsening or removing voxels in less important regions. This dynamic adaptation allows the system to maintain high accuracy where needed while achieving fast overall processing.
3Measurement precision
If dense voxel grids are used throughout the scene, then the reconstruction accuracy is improved, but the computational complexity and memory usage increase significantly
Solution Approach 1:
The patent applies local quality by using variable-resolution voxel grids where different regions of the scene have different levels of detail. Important regions (e.g., foreground objects, areas with motion) use fine-grained voxel grids, while background or static regions use coarse grids. This localized refinement reduces overall computational complexity while maintaining high reconstruction quality where needed.
Solution Approach 2:
The patent segments the continuous 3D space into discrete voxel grids with different resolutions. The scene is divided into multiple octrees or hierarchical voxel levels, where coarse grids cover large areas and fine grids focus on detailed regions. This segmentation allows the system to avoid processing the entire scene at high resolution, significantly reducing training complexity while maintaining reconstruction quality in important areas.
Data Source
AI summary
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.