Convolutional Neural Network Depth Map Generation with Superpixel Segmentation
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Solution Overview
Problem
Conventional methods for generating depth maps from stereoscopic videos are inefficient, relying heavily on manual labor and lacking inter-frame continuity, leading to inconsistent depth maps and poor user perception in 2D-3D video conversions.
Innovation Solution
A method using convolution neural networks that incorporates spatial-temporal consistency by segmenting images into superpixels, establishing similarity matrices, and optimizing multi-frame images to generate temporally continuous depth maps, thereby improving accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to obtain depth maps, then depth map accuracy is improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The system performs automatic depth map generation using convolutional neural networks, allowing the computer to serve itself without human intervention. The CNN model processes video frames autonomously to generate depth maps, eliminating the need for manual annotation while maintaining reasonable accuracy through learned features from training data.
Solution Approach 2:
The patent replaces manual mechanical annotation processes with automated computational methods. Instead of human operators manually drawing depth maps, the system uses CNN-based algorithms to automatically infer depth information from RGB video frames, substituting human labor with machine intelligence.
2Productivity
If single-frame optimization is used in convolution neural network training, then training speed is improved, but inter-frame continuity is lost causing depth map inconsistency
Solution Approach 1:
The patent merges multiple adjacent video frames into a unified training sample structure. Instead of training on isolated single frames, the CNN processes sequences of frames together, allowing the model to learn temporal relationships and maintain consistency across frame boundaries while still achieving efficient training through batch processing.
Solution Approach 2:
The patent adds the temporal dimension to the training process by incorporating multiple frames from the video sequence. The training data structure transitions from 2D single-frame images to 4D spatio-temporal volumes, enabling the network to capture motion patterns and temporal consistency while maintaining training efficiency through proper data organization and processing.
3Productivity
If cue-based depth recovery methods are used, then computational efficiency is improved, but applicability is limited to specific scenes only
Solution Approach 1:
The patent transforms the approach from using fixed hand-crafted cues to learning adaptive parameters through training. The CNN model learns optimal depth estimation parameters and features from diverse training data, allowing it to adapt to various scene types, lighting conditions, and object motions while maintaining computational efficiency through the learned parameterization.
Solution Approach 2:
The patent creates a universal depth estimation system that can handle multiple scene types and conditions. The CNN-based approach learns generalizable features from diverse training data, enabling the same model to effectively process various scenes including static and dynamic objects, different lighting conditions, and diverse camera motions, unlike cue-based methods limited to specific scenarios.
Data Source
AI summary
A method for generating spatial-temporal consistency depth map sequences based on convolutional neural networks for 2D-3D conversion of television works includes steps of: 1) collecting a training set, wherein each training sample thereof includes a sequence of continuous RGB images, and a corresponding depth map sequence; 2) processing each image sequence in the training set with spatial-temporal consistency superpixel segmentation, and establishing a spatial similarity matrix and a temporal similarity matrix; 3) establishing the convolution neural network including a single superpixel depth regression network and a spatial-temporal consistency condition random field loss layer; 4) training the convolution neural network; and 5) recovering a depth maps of a RGB image sequence of unknown depth through forward propagation with the trained convolution neural network; which avoids that clue-based depth recovery method is greatly depended on scenario assumptions, and inter-frame discontinuity between depth maps generated by conventional neural networks.