2 citations · 2 across the 12 of their papers we have counts for
13 papers
SlotDiT: Object-Centric Representations for Diffusion Transformers
Gjergj Plepi, Sven Behnke
Text-conditioned latent diffusion models perform strongly in video generation and are promising backbones for robotic applications. However, existing approaches rely on pixel-level…
VideoPCDNet: Video Parsing and Prediction with Phase Correlation Networks
Noel José Rodrigues Vicente, Enrique Lehner, Angel Villar-Corrales +2
Understanding and predicting video content is essential for planning and reasoning in dynamic environments. Despite advancements, unsupervised learning of object representations an…
OC-SOP: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awareness
Helin Cao, Sven Behnke
Autonomous driving perception faces significant challenges due to occlusions and incomplete scene data in the environment. To overcome these issues, the task of semantic occupancy…
SWA-SOP: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Driving
Helin Cao, Rafael Materla, Sven Behnke
Perception systems in autonomous driving rely on sensors such as LiDAR and cameras to perceive the 3D environment. However, due to occlusions and data sparsity, these sensors often…
Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning
Malte Mosbach, Sven Behnke
Building models responsive to input prompts represents a transformative shift in machine learning. This paradigm holds significant potential for robotics problems, such as targeted…
Feature-Preserving Mesh Decimation for Normal Integration
Moritz Heep, Sven Behnke, Eduard Zell
Normal integration reconstructs 3D surfaces from normal maps obtained e.g. by photometric stereo. These normal maps capture surface details down to the pixel level but require larg…
TextOCVP: Object-Centric Video Prediction with Language Guidance
Angel Villar-Corrales, Gjergj Plepi, Sven Behnke
Understanding and forecasting future scene states is critical for autonomous agents to plan and act effectively in complex environments. Object-centric models, with structured late…
PlaySlot: Learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning
Angel Villar-Corrales, Sven Behnke
Predicting future scene representations is a crucial task for enabling robots to understand and interact with the environment. However, most existing methods rely on videos and sim…
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels
Malte Mosbach, Jan Niklas Ewertz, Angel Villar-Corrales +1
Learning a latent dynamics model provides a task-agnostic representation of an agent's understanding of its environment. Leveraging this knowledge for model-based reinforcement lea…
DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models
Helin Cao, Sven Behnke
Perception systems play a crucial role in autonomous driving, incorporating multiple sensors and corresponding computer vision algorithms. 3D LiDAR sensors are widely used to captu…
MCDS-VSS: Moving Camera Dynamic Scene Video Semantic Segmentation by Filtering with Self-Supervised Geometry and Motion
Angel Villar-Corrales, Moritz Austermann, Sven Behnke
Autonomous systems, such as self-driving cars, rely on reliable semantic environment perception for decision making. Despite great advances in video semantic segmentation, existing…
Grasp Anything: Combining Teacher-Augmented Policy Gradient Learning with Instance Segmentation to Grasp Arbitrary Objects
Malte Mosbach, Sven Behnke
Interactive grasping from clutter, akin to human dexterity, is one of the longest-standing problems in robot learning. Challenges stem from the intricacies of visual perception, th…
SLCF-Net: Sequential LiDAR-Camera Fusion for Semantic Scene Completion using a 3D Recurrent U-Net
Helin Cao, Sven Behnke
We introduce SLCF-Net, a novel approach for the Semantic Scene Completion (SSC) task that sequentially fuses LiDAR and camera data. It jointly estimates missing geometry and semant…