Multi-Stage Multi-Frame Denoising for Neural Radiance Fields
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Solution Overview
Problem
Current machine learning-based denoising frameworks operate on single image frames, limiting their effectiveness in capturing the full potential of multi-image frame captures and failing to generate high-quality 3D information due to inherent information losses in RGB image frames, especially when dealing with larger baselines between image frames.
Innovation Solution
The use of multi-stage multi-frame denoising techniques with neural radiance field networks that exploit similarities within sets of raw image frames for alignment and blending, and different perspectives across sets to generate 3D information from viewpoints and angles not captured in the original frames, utilizing raw image sensor data to reduce texture losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning-based denoising frameworks operate on single image frames, then the processing is simple and fast, but the effectiveness in capturing multi-image frame potential and generating high-quality 3D information is limited
Solution Approach 1:
The patent segments the denoising process into multiple stages: first performing denoising on individual image frames, then performing denoising on blended multi-frame results. This segmentation allows the system to handle complex multi-frame data while maintaining processing efficiency through staged computation.
Solution Approach 2:
The patent transitions from processing single 2D image frames to processing multi-frame data that contains 3D spatial information. By blending multiple frames captured from different viewpoints and using neural radiance field networks, the system adds a temporal and spatial dimension to the denoising process, enabling generation of high-quality 3D information while maintaining acceptable processing speeds.
2Loss of information
If multiple image frames are captured and blended to form a single blended image frame, then more information is captured, but information losses occur due to the blending process
Solution Approach 1:
The patent performs preliminary denoising on individual raw image frames before blending them together. This preliminary action removes noise early in the pipeline, preserving more information during the subsequent blending operation and reducing the need for complex post-processing to recover lost details.
Solution Approach 2:
The patent introduces a neural radiance field network as an intermediary that processes the blended multi-frame data. This intermediary component learns to reconstruct high-quality 3D information from the blended frames, compensating for information losses during blending while managing the overall processing complexity through learned transformations.
3Measurement precision
If neural radiance field networks are trained using blended image frames, then high-quality 3D information can be generated, but the training process becomes more complex and time-consuming
Solution Approach 1:
The patent segments the training process into two phases: first training the neural radiance field network using blended multi-frame data to learn 3D representations, then using this trained knowledge to guide denoising operations on individual frames. This segmentation allows efficient training while maintaining high 3D information quality.
Solution Approach 2:
The patent uses blended multi-frame data for training, which contains more information than single frames but less than the full original multi-frame captures. This partial action approach provides sufficient training data to learn high-quality 3D representations while reducing the computational burden of processing all original frames during training.
Data Source
AI summary
A method includes obtaining, using at least one processing device of an electronic device, raw image frames of a scene. The raw image frames include different sets of raw image frames captured at different viewpoints and different viewing angles relative to the scene. The method also includes performing, using the at least one processing device, blending of each set of raw image frames in order to generate blended image frames of the scene. The method further includes training, using the at least one processing device, a machine learning model using the blended image frames. The machine learning model is trained to generate three-dimensional (3D) information about the scene from viewpoints and viewing angles not captured in the sets of raw image frames.