1 code implementation • 20 Mar 2024 • Lucas Nunes, Rodrigo Marcuzzi, Benedikt Mersch, Jens Behley, Cyrill Stachniss
Our experimental evaluation shows that our method can complete the scene given a single LiDAR scan as input, producing a scene with more details compared to state-of-the-art scene completion methods.
1 code implementation • IRAL 2023 • Rodrigo Marcuzzi, Lucas Nunes, Louis Wiesmann, Elias Marks, Jens Behley, Cyrill Stachniss
Panoptic segmentation of 3D LiDAR scans allows us to semantically describe a vehicle’s environment by predicting semantic classes for each 3D point and to identify individual instances through different instance IDs.
Ranked #3 on 4D Panoptic Segmentation on SemanticKITTI
1 code implementation • CVPR 2023 • Lucas Nunes, Louis Wiesmann, Rodrigo Marcuzzi, Xieyuanli Chen, Jens Behley, Cyrill Stachniss
Especially in autonomous driving, point clouds are sparse, and objects appearance depends on its distance from the sensor, making it harder to acquire large amounts of labeled training data.
no code implementations • IRAL 2021 • Rodrigo Marcuzzi, Lucas Nunes, Louis Wiesmann, Ignacio Vizzo, Jens Behley, Cyrill Stachniss
We propose a novel approach that builds on top of an arbitrary single-scan panoptic segmentation network and extends it to the temporal domain by associating instances across time.
Ranked #5 on 4D Panoptic Segmentation on SemanticKITTI