Monocular Video Depth Consistency Through Scene Flow
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Solution Overview
Problem
Determining depth for monoscopic images is challenging due to the difficulty in distinguishing between camera motion and object motion, leading to inconsistent and flickering depth estimates, especially when both the camera and objects are moving.
Innovation Solution
A hybrid approach using a pre-trained depth model fine-tuned with a scene flow model to generate geometrically and temporally consistent depth images by quantifying 3D and 2D consistency through loss functions, adjusting model parameters to improve depth consistency across a video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If triangulation is applied to monoscopic images, then depth values can be determined, but depth consistency deteriorates due to inability to distinguish camera motion from object motion
Solution Approach 1:
The method performs preliminary motion estimation using optical flow to predict pixel correspondences between frames before performing depth triangulation. This preliminary action establishes expected displacement patterns that guide the subsequent depth estimation process, allowing the system to distinguish between camera-induced and object-induced motion artifacts.
Solution Approach 2:
The method implements an iterative feedback loop where depth estimates from multiple frames are combined with optical flow constraints, and the results are refined through repeated triangulation and consistency checking. The feedback mechanism continuously adjusts depth estimates to maintain temporal coherence across the video sequence.
2Productivity
If standard depth estimation is used for moving objects, then processing speed is maintained, but visual quality deteriorates due to flickering and inconsistencies
Solution Approach 1:
The system performs preliminary optical flow computation and pixel correspondence establishment before depth estimation, preparing motion compensation data in advance. This preliminary action enables faster depth processing by pre-computing the motion constraints that will guide triangulation, rather than performing iterative optimization during depth estimation.
Solution Approach 2:
The method dynamically adapts the depth estimation process by adjusting the number of reference frames and triangulation parameters based on detected motion characteristics. For highly dynamic scenes, the system reduces temporal integration to maintain processing speed, while for static or slowly moving scenes, it increases integration to improve quality.
Data Source
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AI summary
A method includes determining, based on a first image, a first depth of a first pixel and, based on a second image, a second depth of a second pixel that corresponds to the first pixel. The method also includes determining a first 3D point based on the first depth and a second 3D point based on the second depth, and determining a scene flow between the first and second images. The method additionally includes determining an induced pixel position based on a post-flow 3D point representing the first 3D point displaced according to the scene flow, determining a flow loss value based on the induced pixel position and a position of the second pixel and a depth loss value based on the post-flow 3D point and the second 3D point, and adjusting the depth model or the scene flow model based on the flow and depth loss values.