Depth Map Generation Using Temporal Fusion and Uncertainty Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for generating depth maps from videos suffer from temporal incoherence, leading to artifacts like flickering, and require significant computational resources for training machine learning models, making real-time depth map generation impractical.
Innovation Solution
A computer-implemented method that generates temporally-coherent depth maps by processing frames using single-frame estimation, optical flow computation, pose optimization, and a fusion model that combines depth maps and uncertainty maps, eliminating the need for training a machine learning model on the target video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model is trained to generate depth maps from video frames, then temporal coherence of depth maps is improved, but computational cost and training time increase significantly
Solution Approach 1:
The depth map generation process is segmented into two distinct components: a trained machine learning model that provides temporal coherence guidance, and a real-time inference pipeline that generates actual depth maps. The model is trained offline on training data to learn temporal relationships, then used online to guide frame-by-frame depth estimation without requiring full retraining on target video data.
Solution Approach 2:
The machine learning model is trained in advance on training data to learn temporal coherence patterns before being deployed for real-time depth map generation. This preliminary training phase separates the computationally expensive learning process from the real-time inference process, allowing the model to capture temporal relationships without requiring training on the target video itself.
2Reliability
If a machine learning model is trained on the target video to generate depth maps, then temporal coherence is improved, but the process cannot be performed in real-time
Solution Approach 1:
The system separates model training from real-time inference. The machine learning model is trained offline on training data to learn temporal coherence, then deployed for real-time depth map generation where it processes each frame independently using previously learned patterns, achieving both temporal coherence and real-time performance.
Solution Approach 2:
The model learns temporal coherence patterns in advance during offline training on training data. During real-time operation, the pre-trained model applies these learned patterns to generate temporally coherent depth maps without requiring training on the target video, enabling real-time processing.
3Productivity
If each frame is processed separately to generate depth maps, then computational efficiency is improved, but temporal coherence deteriorates causing flickering artifacts
Solution Approach 1:
The system uses feedback from previously processed frames to guide current frame processing. The machine learning model receives information about temporal relationships from training data and applies these learned patterns to maintain consistency across frames, while each frame is still processed independently for computational efficiency.
Solution Approach 2:
The machine learning model acts as an intermediary that translates temporal coherence patterns learned from training data into guidance for real-time depth map generation. It mediates between the need for frame-by-frame independent processing (for efficiency) and the need for temporal consistency (to avoid flickering).
Data Source
AI summary
The disclosed method for generating a first depth map for a first frame of a video includes performing one or more operations to generate a first intermediate depth map based on the first frame and a second frame preceding the first frame within the video, performing one or more operations to generate a second intermediate depth map based on the first frame, and performing one or more operations to combine the first intermediate depth map and the second intermediate depth map to generate the first depth map.