CSPN Depth Refinement for Real-Time Dense Depth Maps
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Solution Overview
Problem
Existing depth estimation methods from a single image suffer from blurry predictions and poor alignment with image structures due to insufficient attention to neighboring pixel relationships, particularly in tasks requiring real-time performance and local context.
Innovation Solution
A convolutional spatial propagation network (CSPN) is used to propagate local areas in multiple directions simultaneously, updating pixel depths in parallel to refine depth maps, incorporating sparse depth samples for improved alignment with image structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If scanline or scan-column propagation is used in SPN, then the network can process depth estimation, but pixels at the right-most column must wait for information from the left-most column, reducing processing speed
Solution Approach 1:
The patent segments the propagation process into multiple independent directional passes (horizontal left-to-right, horizontal right-to-left, vertical top-to-bottom, vertical bottom-to-top). Each pass processes pixels independently without waiting for other passes, eliminating the sequential dependency bottleneck while maintaining comprehensive contextual propagation.
Solution Approach 2:
The patent implements periodic action by performing multiple passes of propagation in different directions. Each pass completes fully before the next begins, creating a periodic processing pattern that eliminates waiting time within each pass while achieving thorough depth refinement through repeated contextual updates.
2Measurement precision
If manually designed affinity is used between neighboring pixels, then the approach is computationally efficient, but the depth predictions become blurry and do not align well with image structures
Solution Approach 1:
The patent introduces an intermediary affinity network that learns optimal affinity weights between pixel pairs based on image content and depth relationships. This learned affinity matrix acts as a mediator that guides the propagation process to preserve sharp boundaries and align with image structures, overcoming the limitations of manually designed affinity while maintaining computational efficiency through sparse representation.
3Measurement precision
If deep fully convolutional neural networks are used for depth estimation, then global scene layout and scales are accurately estimated, but local structure recovery becomes blurry and misaligned with image structures
Solution Approach 1:
The patent replaces the traditional mechanical convolutional refinement approach with a physics-inspired propagation mechanism based on affinity-weighted diffusion. This substitution allows information to flow naturally from high-confidence pixels to low-confidence pixels while respecting image structures, achieving sharp local boundaries without sacrificing global scene understanding.
Solution Approach 2:
The patent dynamically changes the affinity parameters during propagation based on local image characteristics and depth uncertainty. By adapting affinity weights to local contexts, the system enhances boundary preservation in critical regions while maintaining smooth propagation in homogeneous areas, thereby improving local structure alignment without compromising global accuracy.
Data Source
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AI summary
Presented are systems and methods for improving speed and quality of real-time per-pixel depth estimation of scene layouts from a single image by using an end-to-end Convolutional Spatial Propagation Network (CSPN). An efficient linear propagation model performs propagation using a recurrent convolutional operation. The affinity among neighboring pixels may be learned through a deep convolutional neural network (CNN). The CSPN may be applied to two depth estimation tasks, given a single image: (1) to refine the depth output of existing methods, and (2) to convert sparse depth samples to a dense depth map, e.g., by embedding the depth samples within the propagation procedure. The conversion ensures that the sparse input depth values are preserved in the final depth map and runs in real-time and is, thus, well suited for robotics and autonomous driving applications, where sparse but accurate depth measurements, e.g., from LiDAR, can be fused with image data.