astra-vision/MonoScene
[CVPR 2022] "MonoScene: Monocular 3D Semantic Scene Completion": 3D Semantic Occupancy Prediction from a single image
This project helps self-driving car engineers and robotics researchers understand and interact with 3D environments from a single camera image. It takes a standard 2D image and generates a complete 3D scene, detailing the occupancy and semantic labels (e.g., road, car, building) for every voxel in that space. This allows for a rich 3D perception of the surroundings, which is crucial for navigation and object interaction.
799 stars. No commits in the last 6 months.
Use this if you need to reconstruct a detailed 3D environment, including identifying objects and their spatial relationships, using only monocular camera input from urban or indoor scenes.
Not ideal if your application requires real-time processing on embedded systems with limited computational resources, or if you need extremely fine-grained object detection rather than scene-level understanding.
Stars
799
Forks
75
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 06, 2024
Commits (30d)
0
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