3D Reconstruction Learning With Gradient-Weighted Point Training

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Solution Overview

Problem

Existing methods for 3D data representation are not compatible with conventional neural networks for 1D and 2D data, leading to limitations in resolution and effectiveness.

Innovation Solution

A learning method for 3D model reconstruction that extracts points from a learning image, determines weights based on gradient maps, and learns a 3D reconstruction model using these weights to output a 3D model, enhancing compatibility with 2D data without requiring direct 3D input.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional neural networks for 1D and 2D data are used for 3D data processing, then compatibility with existing models is maintained, but resolution and effectiveness are limited

Engineering Contradiction:

Improvecompatibility with existing 2D modelsVSAvoidresolution of 3D reconstruction

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent introduces a gradient map dimension that captures 3D spatial relationships by computing gradients in multiple directions (x, y, z axes) from 2D input images. This additional dimensional information enables conventional 2D neural networks to effectively process 3D reconstruction tasks while maintaining compatibility with existing architectures, thereby resolving the contradiction between model compatibility and reconstruction resolution.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Device complexity

If uniform weights are assigned to all points in 3D reconstruction, then processing is simpler, but boundary clarity and reconstruction accuracy deteriorate

Engineering Contradiction:

Improvesimplicity of weight assignmentVSAvoidclarity of object boundaries

Core Design Contradiction:

Device complexityVSManufacturing precision

Solution Approach 1:

The patent assigns different weights to different points in the 3D reconstruction based on their local characteristics. Points with larger gradient magnitudes (indicating boundary or significant features) receive higher weights, while interior points receive lower weights. This local differentiation approach enhances boundary clarity and reconstruction accuracy without requiring complex global processing mechanisms.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent dynamically adjusts the weight parameter for each point based on the gradient map values. By changing the weight parameter according to local gradient characteristics, the system achieves both simplicity in implementation (through parameter-based control) and high precision in boundary reconstruction (through adaptive parameter adjustment).

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12573141B2Method and device for learning 3D model reconstruction

Publication Date: 2026.03.10 UI (UNIVERSITY IND FOUNDATION) YONSEI UNIVERSITY

AI summary

Disclosed is a learning method for reconstructing a three-dimensional (3D) model performed by at least one processor including extracting a point related to an object in a learning image from the learning image for a 3D reconstruction model, obtaining a gradient map including surrounding context information in three dimensions of the point from a 3D model of the object, determining a weight of the point based on the learning image and the gradient map, and learning the 3D reconstruction model by using the weight such that the 3D model of the object is output from the 3D reconstruction model into which the learning image is entered.