{"id":26403294,"url":"https://github.com/andrewldesousa/mesh-r-cnn","last_synced_at":"2025-08-23T06:03:55.256Z","repository":{"id":161688900,"uuid":"320589767","full_name":"andrewldesousa/Mesh-R-CNN","owner":"andrewldesousa","description":"Group Project for 3D Spatial Learning Practical Course at TUM","archived":false,"fork":false,"pushed_at":"2021-03-29T10:49:20.000Z","size":21931,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-17T15:44:42.121Z","etag":null,"topics":["3d-deep-learning","computervision"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/andrewldesousa.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-12-11T14:01:52.000Z","updated_at":"2023-10-25T15:17:56.000Z","dependencies_parsed_at":"2023-06-15T23:15:20.895Z","dependency_job_id":null,"html_url":"https://github.com/andrewldesousa/Mesh-R-CNN","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/andrewldesousa/Mesh-R-CNN","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrewldesousa%2FMesh-R-CNN","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrewldesousa%2FMesh-R-CNN/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrewldesousa%2FMesh-R-CNN/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrewldesousa%2FMesh-R-CNN/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/andrewldesousa","download_url":"https://codeload.github.com/andrewldesousa/Mesh-R-CNN/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andrewldesousa%2FMesh-R-CNN/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":271745659,"owners_count":24813515,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-23T02:00:09.327Z","response_time":69,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["3d-deep-learning","computervision"],"created_at":"2025-03-17T15:32:55.540Z","updated_at":"2025-08-23T06:03:50.205Z","avatar_url":"https://github.com/andrewldesousa.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Mesh R-CNN Playground - Backend\n\nThis repository contains a web application for interacting with Mesh R-CNN and our additions to the model. The API has been developed using the FastAPI library.\n\n\n![Mesh R-CNN Archiecture](docs/mesh_arch.png)\n\n\nFor more information regarding the original Mesh R-CNN work, visit this [link](https://arxiv.org/abs/1906.02739)\n\n\n## Usage\n### Installing dependencies\nTo install the dependencies for the application, use the requirements.txt file.\n\n`pip install -r requirements.txt` \n\n### Running the application\n\nFor running the app with hot reload functionality.\n\n`uvicorn api.main:app --reload --host 0.0.0.0`\n\nIf you don't need hot reload functionality, then run\n\n`uvicorn api.main:app --host 0.0.0.0`\n\n\n## Functionality\nAfter cloning this repository and setting up the dependencies propery (with requirements.txt), you are able to interact with the model. The API returns everything with the original Mesh R-CNN model plus more. What gets returned are predictions for bounding box, classification, voxel representation, mesh representation, and textured mesh representations.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrewldesousa%2Fmesh-r-cnn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandrewldesousa%2Fmesh-r-cnn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrewldesousa%2Fmesh-r-cnn/lists"}