# US 2022/0284662 A1 · Transforming sensor data to train models used with different sensor configurations > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0284662 A1 - Document ID: US20220284662A1 - Status: Published application - Kind: A1 - Application number: 17/194,382 - Filed: 2021-03-08 - Published: 2022-09-08 - Earliest claimed: 2021-03-08 - Assignees: GM Global Technology Operations LLC - Applicants: GM Global Technology Operations LLC - Inventors: Jacob A. BOND - Web page: https://patentgrep.com/patent/US20220284662A1 ## CPC - G06T 15/20: Perspective computation - G06T 15/205: {Image-based rendering} - G06N 20/00: Machine learning - G06N 3/0475: Generative networks - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system includes a processor and a memory storing instructions which when executed by the processor configure the processor to receive first data from a first set of sensors arranged in a first configuration. The instructions configure the processor to transform the first data to a second data to train a model to recognize third data captured by a second set of sensors arranged in a second configuration. The second configuration is different than the first configuration. The instructions configure the processor to train the model based on the second set of sensors sensing the second data to recognize the third data captured by the second set of sensors arranged in the second configuration. ## Claims ### Claim 1 A system comprising: a processor; and a memory storing instructions which when executed by the processor configure the processor to: receive first data from a first set of sensors arranged in a first configuration; transform the first data to a second data to train a model to recognize third data captured by a second set of sensors arranged in a second configuration, wherein the second configuration is different than the first configuration; train the model based on the second set of sensors sensing the second data to recognize the third data captured by the second set of sensors arranged in the second configuration; detect one or more objects in the first data; separate the objects from background in the first data; transform perspectives of the objects from 2D to 3D using a machine learning based model; and transform a perspective of the background from 2D to 3D using a computer graphics technique. ### Claim 2 (depends on 1) The system of claim 1 wherein the trained model recognizes the third data captured by the second set of sensors arranged in the second configuration. ### Claim 3 (depends on 1) The system of claim 1 wherein at least one of the second set of sensors is different than at least one of the first set of sensors. ### Claim 4 (canceled) ### Claim 6 (depends on 1) The system of claim 1 wherein the instructions configure the processor to combine the transformed perspectives of the objects and the transformed perspective of the background to generate a 3D scene representing the first data. ### Claim 7 (depends on 6) The system of claim 6 wherein the instructions configure the processor to train the model based on the second set of sensors sensing the 3D scene representing the first data. ### Claim 8 (depends on 5) The system of claim 5 wherein the instructions configure the processor to: generate 2D representations of the 3D perspectives of the objects sensed by the second set of sensors; and generate a 2D representation of the 3D perspective of the background sensed by the second set of sensors. ### Claim 9 (depends on 8) The system of claim 8 wherein the instructions configure the processor to combine the 2D representations of the 3D perspectives of the objects and the 2D representation of the 3D perspective of the background sensed by the second set of sensors. ### Claim 10 (depends on 9) The system of claim 9 wherein the instructions configure the processor to train the model based on the combination of the 2D representations of the 3D perspectives of the objects and the 2D representation of the 3D perspective of the background sensed by the second set of sensors. ### Claim 11 A method comprising: receiving first data from a first set of sensors arranged in a first configuration; transforming the first data to a second data to train a model to recognize third data captured by a second set of sensors arranged in a second configuration, wherein the second configuration is different than the first configuration; and training the model based on the second set of sensors sensing the second data to recognize the third data captured by the second set of sensors arranged in the second configuration detecting one or more objects in the first data; separating the objects from background in the first data; transforming perspectives of the objects from 2D to 3D using a machine learning based model; and transforming a perspective of the background from 2D to 3D using a computer graphics technique. ### Claim 12 (depends on 11) The method of claim 11 further comprising recognizing, using the trained model, the third data captured by the second set of sensors arranged in the second configuration. ### Claim 13 (depends on 11) The method of claim 11 wherein at least one of the second set of sensors is different than at least one of the first set of sensors. ### Claim 14 (canceled) ### Claim 16 (depends on 11) The method of claim 11 further comprising combining the transformed perspectives of the objects and the transformed perspective of the background to generate a 3D scene representing the first data. ### Claim 17 (depends on 16) The method of claim 16 further comprising training the model based on the second set of sensors sensing the 3D scene representing the first data. ### Claim 18 (depends on 15) The method of claim 15 further comprising: generating 2D representations of the 3D perspectives of the objects sensed by the second set of sensors; and generating a 2D representation of the 3D perspective of the background sensed by the second set of sensors. ### Claim 19 (depends on 18) The method of claim 18 further comprising combining the 2D representations of the 3D perspectives of the objects and the 2D representation of the 3D perspective of the background sensed by the second set of sensors. ### Claim 20 (depends on 19) The method of claim 19 further comprising training the model based on the combination of the 2D representations of the 3D perspectives of the objects and the 2D representation of the 3D perspective of the background sensed by the second set of sensors.