4 papers · 1 filter
Emergent 3D Instance Segmentation from Self-Supervised Point Transformers
Ted Lentsch, Santiago Montiel-Marín, Holger Caesar +1
Unsupervised 3D instance segmentation of outdoor LiDAR scans has traditionally relied on handcrafted geometric priors such as density-based clustering, motion cues, or projected 2D…
TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving
Miguel Antunes-García, Santiago Montiel-Marín, Fabio Sánchez-García +3
Bird's-Eye View (BEV) end-to-end instance prediction has emerged as a robust paradigm for autonomous driving perception, effectively mitigating the error propagation inherent in tr…
BEVPredFormer: Spatio-temporal Attention for BEV Instance Prediction in Autonomous Driving
Miguel Antunes-García, Santiago Montiel-Marín, Fabio Sánchez-García +3
A robust awareness of how dynamic scenes evolve is essential for Autonomous Driving systems, as they must accurately detect, track, and predict the behaviour of surrounding obstacl…
TerraSeg: Self-Supervised Ground Segmentation for Any LiDAR
Ted Lentsch, Santiago Montiel-Marín, Holger Caesar +1
LiDAR perception is fundamental to robotics, enabling machines to understand their environment in 3D. A crucial task for LiDAR-based scene understanding and navigation is ground se…