# US 2026/0073633 A1 · Optimizing environment mapping with depth prediction > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0073633 A1 - Document ID: US20260073633A1 - Status: Published application - Kind: A1 - Application number: 19/318,582 - Filed: 2025-09-04 - Published: 2026-03-12 - Earliest claimed: 2024-09-06 - Assignees: Field AI, Inc. - Applicants: Field AI, Inc. - Inventors: Chanyoung CHUNG; Amirreza SHABAN - Web page: https://patentgrep.com/patent/US20260073633A1 ## CPC - G06T 17/05: Geographic models - G06T 7/50: Depth or shape recovery - G06T 17/00: Three-dimensional [3D] modelling for computer graphics - G06V 20/70: Labelling scene content, e.g. deriving syntactic or semantic representations - G06T 2207/10024: Color image - G06T 2207/10028: Range image; Depth image; 3D point clouds - G06T 2207/20081: Training; Learning - G06V 2201/07: Target detection - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A method of and system for generating a three-dimensional map of an environment can include obtaining a first visual data set, generating a depth prior based on the first visual data set, refining a depth prediction model based on the depth prior, generating a layout based on a refined depth prediction model, and constructing a continuous three-dimensional map of the environment based on the layout and aggregated depth measurements. The visual data set can include visual imagery data and depth data. The depth prior can include geometric cues and semantic cues ## Claims ### Claim 1 A method of generating a three-dimensional map of an environment, comprising: obtaining a first visual data set, wherein the first visual data set includes visual imagery data and depth data; generating a depth prior based on the first visual data set, wherein the depth prior includes geometric cues and semantic cues; refining a depth prediction model based on the depth prior; generating a layout based on a refined depth prediction model; and constructing a continuous three-dimensional map of the environment based on the layout and aggregated depth measurements. ### Claim 2 (depends on 1) The method of claim 1, further comprising: receiving an environment data set including at least one of building plans, blueprints, and building information models; and receiving motion data including at least one of movement data and inertial data. ### Claim 3 (depends on 1) The method of claim 1, further comprising: recognizing objects within the environment based on the semantic cues and the geometric cues; and inferring portions of the environment hidden by occlusion based on the semantic cues and the geometric cues. ### Claim 4 (depends on 1) The method of claim 1, wherein refining a depth prediction model includes: receiving a second visual data set via at least one depth sensor and at least one camera; and updating an initial depth prediction model based on the second visual data set and a supervisory data set containing at least one item selected from the group consisting of layout boundaries, object sizes, and space classifications to produce the refined depth prediction model. ### Claim 5 (depends on 1) The method of claim 1, wherein generating the layout includes combining the refined depth prediction model and three-dimensional observation data. ### Claim 6 (depends on 1) The method of claim 1, wherein constructing a continuous three-dimensional map of the environment includes translating the layout and aggregated depth measurements into an ellipsoid data set comprising a plurality of ellipsoids, wherein each ellipsoid in the ellipsoid data set comprises position data and covariance data. ### Claim 7 (depends on 6) The method of claim 6, wherein translating the layout and aggregated depth measurements includes aggregating the ellipsoid data set into a continuous three-dimensional function. ### Claim 8 A system comprising: a processor; and a memory in communication with the processor, the memory comprising executable instructions that, when executed by the processor, cause the system to perform functions of: obtaining a first visual data set, wherein the first visual data set includes visual imagery data and depth data; generating a depth prior based on the first visual data set, wherein the depth prior includes geometric cues and semantic cues; refining a depth prediction model based on the depth prior; generating a layout based on a refined depth prediction model; and constructing a continuous three-dimensional map of an environment based on the layout and aggregated depth measurements. ### Claim 9 (depends on 8) The system of claim 8, wherein the memory further comprises executable instructions that, when executed by the processor, cause the system to perform functions of: receiving an environment data set, including at least one of building plans, blueprints, and building information models; and receiving motion data, including at least one of movement data and inertial data. ### Claim 10 (depends on 8) The system of claim 8, wherein the memory further comprises executable instructions that, when executed by the processor, cause the system to perform functions of: recognizing objects within the environment based on the semantic cues and the geometric cues; and inferring portions of the environment hidden by occlusion based on the semantic cues and the geometric cues. ### Claim 11 (depends on 8) The system of claim 8, wherein refining a depth prediction model includes receiving a second visual data set via at least one depth sensor and at least one camera; and updating an initial depth prediction model based on the second visual data set and a supervisory data set containing at least one item selected from the group consisting of layout boundaries, object sizes, and space classifications. ### Claim 12 (depends on 8) The system of claim 8, wherein generating the layout includes combining the refined depth prediction model and three-dimensional observation data. ### Claim 13 (depends on 8) The system of claim 8, wherein constructing a continuous three-dimensional map of the environment includes translating the layout and aggregated depth measurements into an ellipsoid data set comprising a plurality of ellipsoids, wherein each ellipsoid in the ellipsoid data set comprises position data and covariance data. ### Claim 14 (depends on 13) The system of claim 13, wherein translating the layout and aggregated depth measurements includes aggregating the ellipsoid data set into a continuous three-dimensional function. ### Claim 15 A non-transitory computer readable medium on which are stored instructions that when executed cause a programmable device to: obtain a first visual data set, wherein the visual data set includes visual imagery data and depth data; generate a depth prior based on the first visual data set, wherein the depth prior includes geometric cues and semantic cues; refine a depth prediction model based on the depth prior; generate a layout based on a refined depth prediction model; and construct a continuous three-dimensional map of an environment based on the layout and aggregated depth measurements. ### Claim 16 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the instructions when executed further cause the programmable device to: receive an environment data set, wherein the environment data set includes at least one of building plans, blueprints, and building information models; and receive motion data, wherein the motion data comprises at least one of movement data and inertial data. ### Claim 17 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the instructions when executed further cause the programmable device to: recognize objects within the environment based on the semantic cues and the geometric cues; and infer portions of the environment hidden by occlusion based on the semantic cues and the geometric cues. ### Claim 18 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the refined depth prediction model is based on a second visual data set received via at least one depth sensor and at least one camera; and updating an initial depth prediction model based on the second visual data set and a supervisory data set containing at least one item selected from the group consisting of layout boundaries, object sizes, and space classifications. ### Claim 19 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the layout is based on combining the refined depth prediction model and three-dimensional observation data. ### Claim 20 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the continuous three-dimensional map of the environment includes a continuous three-dimensional function based on aggregation of an ellipsoid data set including a plurality of ellipsoids, wherein each ellipsoid in the ellipsoid data set comprises position data and covariance data.