Scaled Depth Map Estimation via Probabilistic Latent Sampling
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Solution Overview
Problem
Existing systems for estimating depth maps from images face challenges with rigid representations, arbitrary scales, and lack of local specificity, leading to unreliable scaling and increased computing costs.
Innovation Solution
The proposed system uses a learning model that generates multiple depth maps from a single image by maintaining latent vectors and sampling conditioned latent representations, allowing for zero-shot learning and transferability across different camera specifications and scene geometries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deterministic representations are generated from multiple images, then depth estimation can be performed, but the representations become overly rigid and lose local specificity
Solution Approach 1:
The system transitions from deterministic to probabilistic representations, allowing the depth estimates to dynamically adapt to local variations in the scene. The probabilistic framework enables the model to express uncertainty and adapt to different scene conditions, resolving the rigidity issue while maintaining reliability through the learned probability distributions.
Solution Approach 2:
The invention changes the fundamental parameter of representation from deterministic values to probability distributions. This parameter change allows the system to capture local specificity and scene variations that deterministic representations miss, while the learned distributions maintain overall reliability through statistical properties.
2Measurement precision
If multiple images are processed to generate depth maps, then depth estimation is achieved, but computing costs increase
Solution Approach 1:
The system segments the depth estimation task into encoding and decoding phases, where the encoder processes a single image to extract features, and the decoder generates multiple depth map variations. This segmentation eliminates the need to process multiple images while maintaining accuracy through the probabilistic decoding process that samples from learned distributions.
Solution Approach 2:
Instead of processing multiple actual images, the system creates synthetic copies of depth maps by sampling from the learned probability distribution. This copying approach generates multiple plausible depth estimates without the computational cost of processing multiple input images, achieving both accuracy and efficiency.
3Manufacturing precision
If supervised training is used for scaling depth maps, then scaling can be achieved, but the system is geometrically limited to arbitrary scales
Solution Approach 1:
The probabilistic encoder-decoder framework serves multiple functions: it performs depth estimation, uncertainty quantification, and domain adaptation. The universal nature of the probability distribution representation allows the same model to handle different camera geometries and scales without retraining, achieving both precision and versatility through the learned latent space.
4Productivity
If rigid representations are used, then computational efficiency is maintained, but local specificity and detail are lost
Solution Approach 1:
The system adds a probabilistic dimension to the depth representations, transforming scalar depth values into probability distributions. This dimensional enhancement allows the model to capture local scene details and uncertainties without significantly increasing computational complexity, as the probabilistic operations can be efficiently implemented through sampling and aggregation.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to estimating scaled depth maps by sampling variational representations of an image using a learning model. In one embodiment, a method includes encoding data embeddings by a learning model to form conditioned latent representations using attention networks, the data embeddings including features about an image from a camera and calibration information about the camera. The method also includes computing a probability distribution of the conditioned latent representations by factoring scale priors. The method also includes sampling the probability distribution to generate variations for the data embeddings. The method also includes estimating scaled depth maps of a scene from the variations at different coordinates using the attention networks.