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Found 6 papers, 5 papers with code
Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion
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.
Open-World Semantic Segmentation Including Class Similarity
Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles.
Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences
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
Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving
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.
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments.
Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans
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
