3D Scene Reconstruction With Surface-Selective Point Clouds
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Solution Overview
Problem
Current 3D scene reconstruction techniques using deep learning are computationally and memory-intensive, leading to long processing times or infeasibility on certain devices due to the high resource demands of voxel grids, especially when finer detail is required.
Innovation Solution
Convert a voxel grid representing a scene into a sparser point cloud by selecting a subset of voxels based on their proximity to object surfaces, using depth information, and process the point cloud with a point-voxel convolutional neural network to reconstruct the 3D scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a dense voxel grid is used to represent the scene, then the 3D reconstruction captures fine details, but the computing and memory resource requirements increase significantly
Solution Approach 1:
The patent extracts only the relevant subset of voxels from the complete voxel grid by identifying and selecting voxels that are within a threshold distance of object surfaces. This extraction process removes unnecessary empty voxels while preserving the voxels needed for accurate 3D reconstruction, thereby reducing computational load without sacrificing detail quality
Solution Approach 2:
The patent applies local quality by differentiating between surface-proximal voxels and empty voxels, processing only the former with high detail while ignoring the latter. This selective processing approach allocates computational resources locally to where they are most needed (at object surfaces) rather than uniformly across the entire voxel grid
2Manufacturing precision
If the voxel grid resolution is increased to capture finer detail, then the 3D reconstruction accuracy improves, but the processing time and resource consumption increase
Solution Approach 1:
By extracting and selecting only the subset of voxels that are within a threshold distance of object surfaces, the patent eliminates the need to process the entire high-resolution voxel grid. This extraction maintains high reconstruction accuracy for visible surfaces while dramatically reducing the number of voxels requiring computation, thus shortening processing time
3Productivity
If traditional dense neural network layers are used to process the voxel grid, then the 3D reconstruction is performed, but the computational overhead is high due to processing all voxels including empty ones
Solution Approach 1:
The patent applies extraction by identifying and removing empty voxels from the processing pipeline before they reach the neural network. By selecting only surface-proximal voxels and converting them to a point cloud representation, the system ensures that the neural network processes only relevant data, eliminating wasted computation on empty space
Solution Approach 2:
Instead of processing the complete voxel grid through dense neural network layers and then filtering results, the patent inverts the approach by first selecting and converting relevant voxels to a point cloud, then processing only this reduced set. This reversal of the processing order eliminates unnecessary computations from the outset
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for performing 3D scene reconstruction. Such techniques may include obtaining a plurality of voxels of a 3D voxel grid representing a scene including one or more objects; identifying a subset of voxels, from the plurality of voxels, that are within a threshold distance of one or more surfaces of the one or more objects based on depth information associated with a plurality of two-dimensional (2D) images of the scene; generating a point cloud comprising a set of point data structures corresponding to the subset of voxels; and processing the point cloud to reconstruct a 3D representation of the scene.