Diffusion-Based Depth Mapping for Metric Scale Recovery
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Solution Overview
Problem
Systems using monocular cameras face challenges in accurately estimating metric depth due to scale ambiguity and the lack of diverse training data, leading to inaccurate depth estimates.
Innovation Solution
A diffusion-based model that incorporates geometric embeddings and image embeddings to condition noise, allowing for efficient depth map estimation using sparse and unstructured data, leveraging self-attention layers to process information locally and globally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a learning model is used to estimate depth from monocular camera data, then depth prediction capability is improved, but accuracy of metric depth estimation deteriorates due to scale ambiguity
Solution Approach 1:
The patent introduces an intermediary scaling factor that mediates between the learning model's depth predictions and the actual metric depth values. This scaling factor acts as a bridge that resolves the scale ambiguity problem, allowing the model to learn depth relationships without requiring explicit metric scale information during training.
Solution Approach 2:
The patent changes the parameter representation by working with relative depth values and scaling factors rather than direct metric depth values. This parameter transformation allows the model to learn scale-invariant features while maintaining the ability to recover metric depth through the learned scaling relationships.
2Measurement precision
If dense labeled training data is used to train the learning model, then depth estimation accuracy is improved, but data acquisition cost and computational complexity increase
Solution Approach 1:
The patent uses synthetic depth maps generated from synthetic images as training data copies. Instead of requiring expensive dense labeled real-world data, the system creates artificial training examples by rendering synthetic scenes with known depth information, then uses these copies to train the learning model effectively.
Solution Approach 2:
The system performs self-service by generating its own training data through synthetic scene rendering. The model creates its own labeled training examples by synthesizing images and corresponding depth maps, eliminating the need for external dense labeled datasets and reducing data acquisition complexity.
3Adaptability or versatility
If diffusion-based modeling is used for depth prediction, then ability to work with sparse data is improved, but computational cost increases
Solution Approach 1:
The patent segments the depth prediction problem into two distinct stages: first using a lightweight learning model to generate initial depth predictions from sparse input data, then applying diffusion-based refinement only where needed. This segmentation reduces overall computational cost by avoiding full diffusion processing everywhere.
Solution Approach 2:
The patent applies partial diffusion processing rather than full diffusion modeling across the entire image. By using diffusion only for refinement in specific regions or at specific stages, the system achieves the benefit of sparse data handling while avoiding the excessive computational cost of complete diffusion processing.
Data Source
AI summary
System, methods, and other embodiments described herein relate to estimating a depth map from an image using a diffusion model that efficiently learns in optimal computational spaces using a learning model and trains with sparse data. In one embodiment, a method includes estimating a local vector for an image by combining random noise, an image embedding about the image, and a geometric embedding using a learning model, the local vector having depth information at a pixel-level for the image. The method also includes predicting a global vector by combining the image embedding and the geometric embedding at a scene-level by the learning model. The method also includes inferring a depth map of the image by combining the local vector and the global vector using a diffusion model.