9 papers · 1 filter
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…
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…