Less: Label-efficient semantic segmentation for lidar point clouds
Semantic segmentation of LiDAR point clouds is an important task in autonomous driving.
However, training deep models via conventional supervised methods requires large …
However, training deep models via conventional supervised methods requires large …
Slim: Self-supervised lidar scene flow and motion segmentation
SA Baur, DJ Emmerichs, F Moosmann… - Proceedings of the …, 2021 - openaccess.thecvf.com
Recently, several frameworks for self-supervised learning of 3D scene flow on point clouds
have emerged. Scene flow inherently separates every scene into multiple moving agents …
have emerged. Scene flow inherently separates every scene into multiple moving agents …
A systematic literature review on long‐term localization and mapping for mobile robots
Long‐term operation of robots creates new challenges to Simultaneous Localization and
Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes …
Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes …
Ogc: Unsupervised 3d object segmentation from rigid dynamics of point clouds
In this paper, we study the problem of 3D object segmentation from raw point clouds. Unlike
all existing methods which usually require a large amount of human annotations for full …
all existing methods which usually require a large amount of human annotations for full …
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.
Separating moving objects from static ones is essential for navigation, pose estimation, and …
Separating moving objects from static ones is essential for navigation, pose estimation, and …
Hierarchical point-based active learning for semi-supervised point cloud semantic segmentation
Impressive performance on point cloud semantic segmentation has been achieved by fully-
supervised methods with large amounts of labelled data. As it is labour-intensive to acquire …
supervised methods with large amounts of labelled data. As it is labour-intensive to acquire …
Improving nighttime driving-scene segmentation via dual image-adaptive learnable filters
Semantic segmentation on driving-scene images is vital for autonomous driving. Although
encouraging performance has been achieved on daytime images, the performance on …
encouraging performance has been achieved on daytime images, the performance on …
Banana: Banach fixed-point network for pointcloud segmentation with inter-part equivariance
Equivariance has gained strong interest as a desirable network property that inherently
ensures robust generalization. However, when dealing with complex systems such as …
ensures robust generalization. However, when dealing with complex systems such as …
Human body shape completion with implicit shape and flow learning
In this paper, we investigate how to complete human body shape models by combining
shape and flow estimation given two consecutive depth images. Shape completion is a …
shape and flow estimation given two consecutive depth images. Shape completion is a …
Adv3d: Generating safety-critical 3d objects through closed-loop simulation
Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to
ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate …
ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate …