ZERO-SHOT MONOCULAR DEPTH ESTIMATION USING GENERATIVE ARTIFICIAL INTELLIGENCE MODELS

Number: 20250363650
Published: 2025-11-27
Filed: 2025-05-16
Assignee: DISNEY ENTERPRISES, INC.
Inventors: SCHROERS; Christopher Richard et al.
CPC: G06T7/50; G06N3/0455; G06N3/0475; G06N3/088; G06T7/30
Verdict: Set aside generic computer-vision depth-estimation research
Source: Google Patents · FreePatentsOnline

Abstract

Embodiments of the present disclosure provide techniques for training a generative artificial intelligence model to generate depth estimates for an input image. An example method generally includes generating a coarse depth map from an input image in a training data set. The coarse depth map is aligned based on a ground-truth depth map corresponding to the input image in the training data set. A masked depth map is generated based on distances calculated between different portions of the aligned coarse depth map. A generative artificial intelligence model is trained to perform monocular depth estimation on an image of a scene based on the input image and the masked depth map. The trained generative artificial intelligence model is deployed.

Background

BACKGROUND Field of the Various Embodiments

Embodiments of the present disclosure relate generally to computer vision and machine learning and, more specifically, to techniques for depth estimation for monocular images. Description of the Related Art

Depth information is used in various tasks, such as autonomous driving, robotics, digital graphics rendering, and the like. Depth information can be obtained various inputs, such as ranging data inputs (e.g., from radar, light detection and ranging (LIDAR) sensors, etc.) or image data. For image data, depth information can be easily obtained from stereo imagery. However, obtaining depth information from monocular images (e.g., single-view images) is complicated task.

To obtain depth information for a scene captured in a monocular image, machine learning models can be trained to use geometric prior information learned from a training data set to generate depth information for an input image. The images in a training data set used to train such a model may be generalized across a variety of scenes included in the training data set. However, because the training data set may lack fine-grained depth data, the depth data associated with images in the training data set may be coarse, noisy, and incomplete. Thus, machine learning models trained to generate estimated depth information for an input image may be generalized and coarse or specific to a particular environment and more detailed.

To address tradeoff

Claims

1. A processor-implemented method, comprising: generating a coarse depth map from an input image in a training data set; aligning the coarse depth map based on a ground-truth depth map corresponding to the input image in the training data set; generating a masked depth map based on distances calculated between different portions of the aligned coarse depth map; training a generative artificial intelligence model to perform monocular depth estimation on an image of a scene based on the input image and the masked depth map; and deploying the trained generative artificial intelligence model. || 10. A processor-implemented method, comprising: generating a coarse depth map from an input image; generating a latent space representation of a fine depth map for the input image based on a generative artificial intelligence model, the input image, the coarse depth map, and a noise input; decoding the fine depth map from the latent space representation; and outputting the fine depth map. || 16. A processing system, comprising: at least one memory having executable instructions stored thereon; and one or more processors configured to execute the executable instructions to cause the processing system to: generate a coarse depth map from an input image in a training data set; align the coarse depth map based on a ground-truth depth map corresponding to the input image in the training data set; generate a masked depth map based on distances calculated between different portions of the aligned coarse depth map; train a generative artificial intelligence model to perform monocular depth estimation on an image of a scene based on the input image and the masked depth map; and deploy the trained generative artificial intelligence model.