{"id":19134125,"url":"https://github.com/um-arm-lab/arm_clouds","last_synced_at":"2026-06-19T19:30:55.967Z","repository":{"id":226506732,"uuid":"768827255","full_name":"UM-ARM-Lab/arm_clouds","owner":"UM-ARM-Lab","description":"Simple module for convenient NumPy and PyTorch point cloud utilities","archived":false,"fork":false,"pushed_at":"2024-03-12T21:13:00.000Z","size":18,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":9,"default_branch":"main","last_synced_at":"2025-11-12T19:08:18.605Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/UM-ARM-Lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-03-07T20:07:08.000Z","updated_at":"2024-06-24T01:12:57.000Z","dependencies_parsed_at":"2024-03-08T00:21:29.077Z","dependency_job_id":"53fa8b70-c44b-469e-97de-052cd5a932c4","html_url":"https://github.com/UM-ARM-Lab/arm_clouds","commit_stats":null,"previous_names":["um-arm-lab/arm_clouds"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/UM-ARM-Lab/arm_clouds","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UM-ARM-Lab%2Farm_clouds","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UM-ARM-Lab%2Farm_clouds/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UM-ARM-Lab%2Farm_clouds/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UM-ARM-Lab%2Farm_clouds/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/UM-ARM-Lab","download_url":"https://codeload.github.com/UM-ARM-Lab/arm_clouds/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UM-ARM-Lab%2Farm_clouds/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34546191,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-19T02:00:06.005Z","response_time":61,"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":[],"created_at":"2024-11-09T06:25:32.092Z","updated_at":"2026-06-19T19:30:55.945Z","avatar_url":"https://github.com/UM-ARM-Lab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# `arm_clouds`\n\nA simple module for convenient usage of both NumPy and\nPyTorch point clouds. Functionality includes:\n- Visualization in Plotly, Open3D, and Rerun with one function call.\n- Tracks and enforces consistency between XYZ and (optional) RGB PyTorch data type and device.\n- Methods for (un)normalization of RGB values.\n\n# Installation\n\nTo install `arm_clouds`, clone the repository and install with `pip install -e .`.\n\n# Usage\n\nThis repo includes classes for both single point clouds (`PointCloud`) and point cloud lists (`PointCloudList`).\n\n## `PointCloud` Usage\n\nAn example `PointCloud`:\n```python\n# Your XYZ and RGB data\nxyz = torch.rand(3, 100)\nrgb = torch.rand(3, 100)  # optional\n\npc = PointCloud(xyz, rgb)\n\n# One-call-visualization. Can also visualize in rerun and open3d.\npc.visualize_plotly()\n```\n\n## `PointCloudList` Usage\n\nYou can initialize a `PointCloudList` from a NumPy array or PyTorch tensor of all point clouds:\n```python\nxyz = np.random.rand(100, 3, 25)\npcl = PointCloudList(xyz)\npcl.visualize_plotly()\n```\n\nOr if you're dealing with point clouds with different numbers of points:\n```python\npcl = PointCloudList()\n\n# If you haven't already created a `PointCloud` from the raw data:\nfor xyz, rgb in zip(xyzs, rgbs):\n    pcl.append_create_cloud(xyz, rgb)\n\n# If you already have your data in the `PointCloud` format, you can do:\nfor pc in point_cloud_python_list:\n    pcl.append(pc)\n# But if you already have multiple `PointCloud` objects in a raw Python list,\n# you can directly initialize the `PointCloudList`:\npcl = PointCloudList(point_cloud_python_list)\n```\n\nFor further usage examples, look at the [test functions](./tests/).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fum-arm-lab%2Farm_clouds","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fum-arm-lab%2Farm_clouds","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fum-arm-lab%2Farm_clouds/lists"}