Deepen 4d: LiDAR Point level instance and semantic segmentation

Hi Product Hunt community, Let me introduce you Deepen 4D. In order to develop safe and reliable Autonomous Driving Systems, there is the need to collect and label a massive amount of data. This data comes from all the sensors part of the vehicles perception suite: cameras, radars and LiDARS are the most used. Annotating all the sequences of frames collected in this way can take up to 800 man hours for a single mile of driving. While sequences image annotation can leverage a high level of automation also for semantic segmentation, as of today it is still a challenge to semantically segment long sequences of ground truth LiDAR data in a way that is fast, accurate and efficient — not only in terms of human labor but also in terms of computational efficiency. This is a huge problem for the whole autonomous vehicle industry. On one hand, semantically segmented LiDAR data can bring a ton of advantages and unlock a wide array of innovations and advances in the field. On the other hand, it is too cumbersome, time-consuming, resource-demanding and expensive to have LiDAR semantic segmentation available at large. It is arguable that the unavailability and inaccessibility of LiDAR dataset with semantic segmentation is holding back AV companies to reach important milestones in their quest to autonomy. Deepen’s 4D LiDAR annotation technology is solving the issue by making semantic and instance segmentation of long sequences of LiDAR data highly efficient and accurate. Now it is possible to segment those long sequences in minimal time and with exceptional results, compared to what was available yesterday. Autonomy is one step closer!