Exposure-Guided 3D Reconstruction for Saturated LDR Scenes
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Solution Overview
Problem
Existing 3D reconstruction techniques struggle to accurately reconstruct 3D scenes with high dynamic range (HDR) illumination using low dynamic range (LDR) images, leading to inaccuracies and artifacts due to overexposed or underexposed regions, which current methods fail to effectively mitigate.
Innovation Solution
A method and system for exposure-guided 3D reconstruction that uses a pre-trained HDR 3D reconstruction model, applies a HDR to LDR tone-mapping operator, and adjusts model parameters through a differentiable render function to de-weight pixel differences of saturated pixels, minimizing loss functions and preventing exposure-based artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LDR images are used for 3D reconstruction, then the reconstruction process is simple and fast, but the accuracy and quality of HDR illumination reconstruction deteriorates due to overexposed and underexposed regions
Solution Approach 1:
The patent applies tone mapping operators to LDR images before 3D reconstruction to pre-process and recover HDR information. This preliminary action transforms the input images to contain enhanced dynamic range data, allowing standard reconstruction algorithms to produce accurate HDR results without requiring complex HDR-specific processing during reconstruction
Solution Approach 2:
The patent modifies the pixel intensity parameters of LDR images by applying tone mapping functions that expand the dynamic range. This parameter transformation converts constrained LDR values into expanded HDR-like values, enabling accurate illumination reconstruction while maintaining compatibility with existing reconstruction pipelines
2Loss of energy
If existing 3D reconstruction techniques are applied to LDR images, then the process is computationally efficient, but artifacts such as floaters and overfitting occur due to exposure ambiguities
Solution Approach 1:
The patent introduces tone mapping operators as an intermediary processing step between LDR image input and 3D reconstruction. This intermediary transforms the ambiguous LDR data into enhanced representations that disambiguate exposure levels, preventing artifacts like floaters while maintaining computational efficiency by using standard reconstruction algorithms
Solution Approach 2:
The patent applies tone mapping preprocessing to counteract the harmful effects of LDR limitations before reconstruction begins. This preliminary anti-action prevents exposure-related artifacts from occurring during reconstruction, eliminating the need for complex artifact correction steps and maintaining computational efficiency
3Adaptability or versatility
If per-pixel scaling is used for exposure compensation, then exposure levels can be unified across images, but saturation from input images cannot be effectively mitigated
Solution Approach 1:
The patent applies different tone mapping parameters and strategies to different regions of images based on their local characteristics. Saturated regions receive specialized handling through adaptive tone mapping that preserves detail, while non-saturated regions use standard unification methods, achieving both exposure consistency and saturation mitigation
Data Source
AI summary
Disclosed is a method for enabling exposure-guided three-dimensional (3D) reconstruction, including (i) receiving Low Dynamic Range (LDR) input image captured by camera; (ii) rendering High Dynamic Range (HDR) image corresponding to input image, using HDR 3D reconstruction model pre-trained for HDR image reconstruction of 3D environment; (iii) applying HDR to LDR tone-mapping operator on HDR image, for producing tone-mapped LDR image; (iv) determining first loss function (FLF) between input image and tone-mapped LDR image, FLF comprising weighted pixel value differences between pixels of input image and corresponding pixels of tone-mapped LDR image; (v) determining whether input image has saturated pixel(s), saturated pixel(s) comprises: highlight saturated pixel, shadow saturated pixel; (vi) de-weighting pixel value difference corresponding to saturated pixel in FLF; (vii) back-propagating gradient of FLF with respect to model parameters, through differentiable render function of HDR 3D reconstruction model, for adjusting model parameters in way that reduces FLF.