Aligned Pointmaps for Uncalibrated 3D Scene Reconstruction
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Solution Overview
Problem
Existing 3D reconstruction methods from images face challenges such as noise introduction due to imperfect sub-problem solutions and the inability to handle the monocular case, lacking robustness in estimating 3D geometry and camera parameters without prior information.
Innovation Solution
A neural network-based method that processes multiple images without extrinsic or intrinsic properties to generate aligned pointmaps, which are used to reconstruct 3D scenes, including pointclouds, camera parameters, and depth maps, utilizing transformer encoders and decoders for alignment and regression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional 3D reconstruction pipelines (SfM, MVS) are used, then 3D geometry can be reconstructed, but noise is introduced due to imperfect sub-problem solutions and the monocular case cannot be solved
Solution Approach 1:
The patent merges multiple sub-problems (depth estimation, pose estimation, 3D reconstruction) into a single unified neural network framework. Instead of sequentially solving keypoint detection, matching, SfM, and MVS as separate sub-problems that introduce noise, the network processes images directly to generate aligned pointmaps and 3D representations, eliminating error accumulation across pipeline stages.
Solution Approach 2:
The neural network is designed to handle multiple scenarios universally - it can process both monocular and multi-view images, and can output various representations (pointmaps, depth maps, 3D meshes, camera parameters) from the same architecture. This universal approach eliminates the limitation of traditional methods that cannot solve the monocular case.
2Ease of manufacture
If extrinsic and intrinsic camera properties are provided, then 3D reconstruction can be performed, but the system cannot handle uncalibrated cameras without prior information
Solution Approach 1:
The system performs self-calibration by automatically estimating camera extrinsic and intrinsic parameters from the input images without requiring prior calibration information. The neural network learns to infer these parameters directly from the image data, enabling the system to handle uncalibrated cameras while maintaining reconstruction accuracy.
3Manufacturing precision
If traditional reconstruction pipelines are used, then 3D scenes can be reconstructed, but the pipeline is complex and cannot be simplified
Solution Approach 1:
The patent consolidates multiple complex processing stages (keypoint detection, feature matching, essential matrix computation, triangulation, dense MVS) into a single integrated neural network. This merging maintains reconstruction quality while dramatically simplifying the pipeline from many sequential steps to one end-to-end process that directly generates 3D representations from images.
Data Source
AI summary
A computer-implemented method for reconstructing a scene in three dimensions from a plurality of images of one or more viewpoints of the scene acquired using an imaging device includes: receiving the plurality of images without receiving extrinsic or intrinsic properties of the imaging device; and processing the plurality of images using a neural network to produce a plurality of pointmaps of the scene that correspond to the plurality of images and that are aligned in a common coordinate frame, where each pointmap is a one-to-one mapping between pixels of one of the plurality of images and three-dimensional points of the scene.