Sparse-Depth Neural Networks for Monocular Depth Scale Disambiguation
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Solution Overview
Problem
Existing machine learning systems face challenges in accurately determining depth scale information, particularly in monocular depth estimation, leading to ambiguity in depth estimation from a single image.
Innovation Solution
A neural network system that utilizes sparse depth inputs, including sparse seed points and a validity map, to generate a feature representation that disambiguates depth scale by propagating scale information through an encoder-decoder architecture, eliminating the need for subsequent re-scaling and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If monocular depth estimation is performed using a single image, then the system is simple and computationally efficient, but depth scale information is ambiguous and accuracy is reduced
Solution Approach 1:
The system performs preliminary depth estimation to generate an initial depth map, then uses this preliminary result to guide subsequent refinement steps. The encoder-decoder architecture pre-processes the image to extract features before applying scale disambiguation, effectively performing required actions in advance to improve final depth accuracy without requiring complex multi-stage processing
Solution Approach 2:
The patent introduces an intermediary mechanism (the neural network encoder-decoder system with specific loss functions) that mediates between the single image input and the depth output. This intermediary processes the image through multiple layers, using auxiliary supervision signals and scale提示信息 to resolve depth scale ambiguity, thereby improving measurement precision without directly modifying the input or output structures
2Measurement precision
If dense depth processing is performed to maintain high spatial resolution, then depth estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the depth estimation process into distinct functional components: an encoder that extracts features from the input image, a decoder that reconstructs the depth map, and auxiliary modules that handle scale information. This segmentation allows each component to be optimized independently, maintaining high spatial resolution in the decoder while keeping computational complexity manageable through efficient feature extraction in the encoder
Solution Approach 2:
The system applies partial processing to different regions of the image based on their importance. The encoder-decoder architecture focuses computational resources on critical depth estimation tasks while using lighter processing for less important areas. Auxiliary loss functions provide additional supervision only where needed, avoiding excessive computation across the entire image while still maintaining high overall accuracy
Data Source
AI summary
Systems and techniques are provided for generating depth information from one or more images. For instance, a method can include obtaining an image of a scene and obtaining depth information associated with one or more objects in the scene. The method can include processing, using an encoder of a neural network model, the image and the depth information to generate a feature representation of the image and the depth information. The method can further include processing, using a decoder of the neural network model, the feature representation of the image and the depth information to generate a depth output corresponding to the image.