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Uncertainty: Overview

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Related Experiment Video

Updated: Jun 26, 2026

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

RIGI: Rectifying Image-to-3D Generation Inconsistency via Uncertainty-Aware Learning.

Jiacheng Wang, Zhedong Zheng, Wei Xu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 24, 2026

Summary

This study introduces an uncertainty-aware framework for image-to-3D generation, improving 3D model quality. It addresses data imperfections by modeling and mitigating epistemic uncertainty for more robust results.

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Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Area of Science:

Computer Vision
3D Reconstruction
Machine Learning

Background:

Image-to-3D generation seeks to create 3D models from single 2D images.
Conventional methods often fail due to epistemic uncertainty from imperfect multi-view data.
Deterministic optimization in existing pipelines neglects crucial uncertainty information.

Purpose of the Study:

To develop an uncertainty-aware optimization framework for more robust and reliable 3D generation.
To explicitly model and mitigate epistemic uncertainty in the image-to-3D pipeline.
To enhance the geometric and perceptual plausibility of generated 3D models.

Main Methods:

Implemented a progressive sampling strategy with varying camera elevations to enhance viewpoint coverage.
Estimated epistemic uncertainty using discrepancies between two independently optimized Gaussian models.
Incorporated an uncertainty map into regularization for adaptive loss weighting, suppressing unreliable supervision.

Main Results:

Significantly reduced artifacts and inconsistencies in generated 3D models.
Demonstrated higher-quality 3D generation compared to conventional approaches.
Provided theoretical analysis with a probabilistic upper bound on expected generation error.

Conclusions:

The proposed uncertainty-aware optimization framework enhances image-to-3D generation robustness.
Explicitly modeling epistemic uncertainty leads to superior 3D model quality.
The method offers a promising direction for reliable 3D reconstruction from single images.