Solving Multi-Sensor Labeling Challenges in Robotics and Automotive
How to build ground-truth data, specifically for autonomous systems in the robotics and automotive industries? Otto, CEO [...]
Inside .lumen: How they scaled their annotation process
.lumen's mission is deeply personal for Cornel Amariei. As the only non-disabled member of his family, Cornel [...]
Late vs early sensor fusion: a comparison
Sensor fusion is the process of combining data from multiple sensors (e.g. multiple cameras, lidars, and radars) to [...]
Annotating Ground Truth in Computer Vision: Vector vs. Segmentation Annotations
Accurately interpreting the environment is crucial in computer vision, especially in fields like autonomous vehicles and robotics. This [...]
Better 3D sequence labeling, deep linking, and more
We’ve been hard at work in the first weeks of the new year to deliver you another round [...]
Building your internal data labeling software
Annotation or labeling tooling has multiple use cases. Think about labelling datasets to use for your ML [...]
Leverage 3D annotations to drastically speed up 2D labeling, and more
In our last newsletter we announced our new multi-sensor labeling interface. This month, we're excited to introduce [...]
What data do you need for multi-sensor labeling?
In autonomous driving and robotics, combining camera images with lidar point cloud data can greatly improve the perception [...]
Evaluating SAM for image segmentation labeling
In robotics and autonomous driving, accurate image segmentation is crucial for extracting meaningful information from visual data. [...]
Zero-shot object detection with OWL-ViT
What if you could detect objects of any type in an image, without having to train a [...]
