3D Point Cloud Generation Using Height Maps and Perspective Fields
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Solution Overview
Problem
Conventional systems struggle to accurately estimate three-dimensional geometry from a single digital image, particularly in real-world scenarios with varied geometry and texture, due to the inability to handle unknown object-ground relationships and camera parameters, leading to inaccurate and inflexible three-dimensional reconstructions.
Innovation Solution
A three-dimensional estimation system that models the ground, object, and camera simultaneously, using a dense representation neural network to generate pixel height maps and perspective field representations, optimizing for object-ground relationships and camera parameters to produce accurate three-dimensional point clouds and depth maps without requiring large-scale training or generic parameter assumptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use multi-view digital imagery to estimate three-dimensional geometry, then measurement precision is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a height map dimension that represents vertical distances from ground to object points, transforming the traditional two-dimensional image coordinates into a three-dimensional representation. This allows single-view images to encode three-dimensional geometric information by adding the height dimension, enabling accurate 3D geometry estimation without requiring multiple views.
Solution Approach 2:
The patent uses perspective fields as an intermediary representation that bridges the gap between two-dimensional image inputs and three-dimensional geometry outputs. The perspective field encodes camera pose, focal length, and object-ground relationships in a structured format that facilitates accurate three-dimensional reconstruction from single images.
2Ease of operation
If conventional systems assume generic camera parameters, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary estimation of camera parameters (pose, focal length) and object-ground relationships from the single image before proceeding to three-dimensional reconstruction. This preliminary action allows the system to work with realistic camera parameters rather than generic assumptions, improving measurement precision while maintaining ease of operation.
Solution Approach 2:
The patent employs iterative optimization that uses feedback from the height map and perspective field consistency to refine camera parameter estimates. The system adjusts camera parameters to maximize the consistency between the predicted and actual image observations, leading to more accurate three-dimensional reconstructions.
3Productivity
If conventional systems process single images without object-ground relationship modeling, then processing speed is improved, but measurement precision worsens
Solution Approach 1:
The patent segments the scene into distinct components: ground plane, object points, and camera system. By modeling the object-ground relationship explicitly, the system can process single images efficiently while maintaining measurement precision, as the segmentation allows for targeted computation of three-dimensional coordinates without requiring complex multi-view processing.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for estimating the three-dimensional geometry of an object in a digital image by modeling the ground, object, and camera simultaneously. In particular, in one or more embodiments, the disclosed systems receive a two-dimensional digital image portraying an object. Further, the systems generate, utilizing a dense representation neural network, an estimate of an object-ground relationship of the object portrayed in the two-dimensional digital image and an estimate of camera parameters for the two-dimensional digital image. Additionally, the systems generate, utilizing a perspective field guided pixel height reprojection model, one or more of a three-dimensional point cloud or a depth map of the object from the estimated object-ground relationship and the estimated camera parameters.