Daniel Havíř

Title: Daniel Havíř and Automated Cuboid Annotation

Introduction

Daniel Havíř is an inventor based in San Francisco, California. His patent work includes a method for automatically pre-labeling point cloud data with cuboid annotations, a process that can support the creation and refinement of labeled data for machine-learning applications.

Latest Patents

Havíř is associated with 1 patent, titled “Pre-labeling data with cuboid annotations.” The invention processes point cloud data with machine-learning models to detect, associate, and localize objects, generating tracks made up of cuboid annotations.

The patent describes an object-detection model that performs coarse localization and is trained with a loss function that separately evaluates differences between predicted and ground-truth cuboid corners for position, size, and yaw. A refinement model then uses two-dimensional projections of regions around the predicted tracks, together with the tracks themselves, to produce more accurate results. The refined tracks are filtered into keyframes, with intervening frames interpolated, and can be presented to a user for review and editing.

Career Highlights

Havíř is associated with Scale AI, Inc. His patent reflects work on machine-learning techniques for organizing and refining point-cloud annotations.

Collaborations

The provided information identifies Chiao-Lun Cheng and Elliot Branson as some of Havíř’s coworkers. It does not specify their roles in the patent or the extent of their collaboration.

Conclusion

Daniel Havíř’s patent describes an automated approach to generating and refining cuboid annotations for point-cloud data, combining machine-learning detection, localization, and human review.