In the near future, autonomous vehicles with full self-driving features will populate our public
roads. However, fully autonomous cars will require robust perception systems to safely …

Recent advancements in vision foundation models (VFMs) have opened up new
possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a …

With the gradual disappearance of the demographic dividend and the labor shortage
brought by the aging population, it is necessary and urgent to achieve a high degree of …

Understanding the scene is key for autonomously navigating vehicles, and the ability to
segment the surroundings online into moving and non-moving objects is a central ingredient …

Current state-of-the-art 3D scene understanding methods are merely designed in a full-
supervised way. However, in the limited reconstruction cases, only limited 3D scenes can be …

Collaborative perception in automated vehicles leverages the exchange of information
between agents aiming to elevate perception results. Previous camera-based collaborative …

Semantic scene interpretation is essential for autonomous systems to operate in complex
scenarios. While deep learning-based methods excel at this task, they rely on vast amounts …

We present f VDB, a novel GPU-optimized framework for deep learning on large-scale 3D
data. f VDB provides a complete set of differentiable primitives to build deep learning …

Abstract Knowledge about the own pose is key for all mobile robot applications. Thus pose
estimation is part of the core functionalities of mobile robots. Over the last two decades …

Abstract 3D panoptic segmentation is a challenging perception task that requires both
semantic segmentation and instance segmentation. In this task, we notice that images could …