Episode Details

This story was originally published on HackerNoon at: https://hackernoon.com/the-hidden-bottlenecks-of-3d-data-labeling.
A “simple” process of the 3D labeling often includes challenges and hidden bottlenecks. How can they be solved, and what is the optimal solution for them?
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #lidar, #3d, #3d-labeling, #3d-point-cloud-navigation, #point-cloud, #data-annotation-services, #good-company, and more.

This story was written by: @keymakr. Learn more about this writer by checking @keymakr's about page, and for more stories, please visit hackernoon.com.

3D labeling unlocks powerful capabilities for autonomous systems across various industries, including automotive, robotics, construction, and healthcare. Point-cloud data is inherently unstable: reflections from glass or wet surfaces, weather-induced noise, and constantly moving objects can distort the scene.