alexklwong/mondi-python
PyTorch Implementation of Monitored Distillation for Positive Congruent Depth Completion (ECCV 2022)
This project helps engineers working with 3D perception tasks to create highly accurate, dense 3D point clouds from sparse inputs. It takes a camera image and a sparse point cloud (like lidar data) and outputs a complete, detailed 3D depth map of the scene. This is useful for robotics engineers, autonomous vehicle developers, or anyone building systems that need to understand 3D environments precisely from sensor data.
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Use this if you need to transform limited 3D sensor data into a full, high-resolution 3D understanding of a physical space, especially when ground truth data is hard to obtain.
Not ideal if you are working with 2D image processing exclusively or if your application does not require dense 3D environment reconstruction.
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Python
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Last pushed
Dec 29, 2024
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