Search Results for author:
Found 19 papers, 14 papers with code
NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving
We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios.
Geometry-Biased Transformer for Robust Multi-View 3D Human Pose Reconstruction
We address the challenges in estimating 3D human poses from multiple views under occlusion and with limited overlapping views.
Polygon Detection for Room Layout Estimation using Heterogeneous Graphs and Wireframes
This paper presents a neural network based semantic plane detection method utilizing polygon representations.
The LuViRA Dataset: Measurement Description
We present a dataset to evaluate localization algorithms, which utilizes vision, audio, and radio sensors: the Lund University Vision, Radio, and Audio (LuViRA) Dataset.
Revisiting the P3P Problem
One of the classical multi-view geometry problems is the so called P3P problem, where the absolute pose of a calibrated camera is determined from three 2D-to-3D correspondences.
LidarCLIP or: How I Learned to Talk to Point Clouds
We also explore zero-shot classification and show that LidarCLIP outperforms existing attempts to use CLIP for point clouds by a large margin.
Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-Rescue
To further mimic the situation on an actual UAV, the agent is not able to observe the search area in its entirety, not even at low resolution, and thus it has to operate solely based on partial glimpses when navigating towards the goal.
Extending GCC-PHAT using Shift Equivariant Neural Networks
Speaker localization using microphone arrays depends on accurate time delay estimation techniques.
Semantic Room Wireframe Detection from a Single View
Reconstruction of indoor surfaces with limited texture information or with repeated textures, a situation common in walls and ceilings, may be difficult with a monocular Structure from Motion system.
Future Object Detection with Spatiotemporal Transformers
In order to make accurate predictions about the future, it is necessary to capture the dynamics in the scene, both object motion and the movement of the ego-camera.
Bootstrapped Representation Learning for Skeleton-Based Action Recognition
In this work, we study self-supervised representation learning for 3D skeleton-based action recognition.
Generic Merging of Structure from Motion Maps with a Low Memory Footprint
We use these representations to perform map merging so that the algorithm is invariant to the merging order and independent of the choice of coordinate system.
Trust Your IMU: Consequences of Ignoring the IMU Drift
In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals.
In Depth Bayesian Semantic Scene Completion
This work studies Semantic Scene Completion which aims to predict a 3D semantic segmentation of our surroundings, even though some areas are occluded.
Efficient Real-Time Radial Distortion Correction for UAVs
In this paper we present a novel algorithm for onboard radial distortion correction for unmanned aerial vehicles (UAVs) equipped with an inertial measurement unit (IMU), that runs in real-time.
FLIP: A Difference Evaluator for Alternating Images
Image quality measures are becoming increasingly important in the field of computer graphics.
Sensor Networks TDOA Self-Calibration: 2D Complexity Analysis and Solutions
Given a network of receivers and transmitters, the process of determining their positions from measured pseudoranges is known as network self-calibration.
Minimal Solvers for Indoor UAV Positioning
In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor UAV navigation.
Beyond Gröbner Bases: Basis Selection for Minimal Solvers
In this paper we show how we can make polynomial solvers based on the action matrix method faster, by careful selection of the monomial bases.
