Monocular 3D Human Reconstruction With Ray-Invariant Features
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Solution Overview
Problem
Existing methods for reconstructing 3D human objects from monocular color images face challenges such as self-occlusion issues, high memory usage, slow execution speed, and high computational demands, particularly in deep learning-based implicit function approaches.
Innovation Solution
A deep learning-based 3D human object reconstruction method that extracts pixel-aligned features and ray-invariant features, using a signed distance function to predict 3D human objects in real time by encoding position information, reducing computation through reuse of ray-invariant features across points on the same ray.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a deep learning-based implicit function is trained to reconstruct 3D human objects, then reconstruction accuracy is improved, but computation time increases significantly
Solution Approach 1:
The patent segments the 3D space into multiple rays originating from the camera center. Instead of processing all points in the 3D space uniformly, the method divides the space into discrete rays and processes points along each ray separately. This segmentation allows the implicit function to be evaluated efficiently along each ray, reducing the overall computational burden while maintaining reconstruction accuracy.
Solution Approach 2:
The patent performs preliminary discretization of the 3D space into rays and pre-processes the monocular image to extract relevant features before the actual reconstruction. By preparing the ray structure and image features in advance, the implicit function evaluation during reconstruction can proceed more quickly without sacrificing accuracy.
2Manufacturing precision
If a method predicts a whole 3D space including the object, then complete 3D reconstruction is achieved, but memory usage and computation increase导致 slow execution speed
Solution Approach 1:
The patent extracts only the necessary information for 3D reconstruction by focusing on ray-based point sampling rather than processing the entire 3D space uniformly. The method extracts signed distance values and color information only at sampled points along rays, eliminating redundant computations in empty or irrelevant regions of the 3D space while still achieving complete object reconstruction.
Solution Approach 2:
Instead of evaluating the implicit function at every possible point in the 3D space, the patent uses partial sampling along rays. It evaluates the function at a subset of points along each ray (using binary search and interpolation), which is less than exhaustive but sufficient to reconstruct the complete 3D object geometry and appearance.
3Productivity
If a depth image prediction method is used, then reconstruction speed is high, but self-occlusion areas cannot be reconstructed
Solution Approach 1:
The patent transitions from 2D depth image prediction to 3D ray-based implicit function evaluation. By moving into the third dimension and evaluating the implicit function along rays in 3D space, the method can infer geometry in self-occluded regions that are not visible in the 2D monocular image, while still maintaining efficient computation through the ray-marching approach.
Data Source
AI summary
There are provided an apparatus and a method for reconstructing a 3D human object in real time based on a monocular color image. A 3D human object reconstruction apparatus according to an embodiment extracts a pixel-aligned feature from a monocular image, extracts a ray-invariant feature from the pixel-aligned feature, generates encoded position information by encoding position information of a point, predicts a SD of a point from the ray-invariant feature and the encoded position information which are extracted, and reconstructs a 3D human object by using the predicted SD. Accordingly, the ray-invariant feature extracted from the pixel-aligned feature, and the encoded position information are used, so that an amount of computation for predicting SDs of points of a 3D space can be noticeably reduced and a speed can be remarkably enhanced.