Abstract 3D object detection is a central task for applications such as autonomous driving, in
which the system needs to localize and classify surrounding traffic agents, even in the …

This work addresses the challenging task of LiDAR-based 3D object detection in foggy
weather. Collecting and annotating data in such a scenario is very time, labor and cost …

Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but
their performance significantly deteriorates in extremely low-light environments. In this work …

For vehicles to navigate autonomously, they need to perceive and understand their
immediate surroundings. Currently, cameras are the preferred sensors, due to their high …

Multimodal sensor fusion is an essential capability for autonomous robots, enabling object
detection and decision-making in the presence of failing or uncertain inputs. While recent …

Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-
resolution 3D depth that is robust to back-scatter in fog, snow, and rain. Instead of …

Reconstructing outdoor 3D scenes from temporal observations is a challenge that recent
work on neural fields has offered a new avenue for. However existing methods that recover …

In this paper, we present a two stages solution to 3D vehicle detection and segmentation.
The first stage depends on the combination of EfficientNetB3 architecture with multiparallel …

Modeling perception sensors is key for simulation based testing of automated driving
functions. Beyond weather conditions themselves, sensors are also subjected to object …

Accurate detection of objects in the three-dimensional world is a core problem for
autonomous vehicles and ensures the safety of the vehicles. Nevertheless, the complex real …