1 citations · 1 across the 7 of their papers we have counts for
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RVLoss: Runoff Vote Loss for Self-Supervised LiDAR Scene Flow Estimation
Shiming Wang, Liangliang Nan, Julian Kooij +2
LiDAR scene flow estimates point-wise motion between two consecutive scans, referred to as the source and target. Leading self-supervised methods typically minimize the Chamfer los…
CorrelationFlow: A Training-Free Geometric Approach for LiDAR Scene Flow Estimation
Minh-Quan Dao, Yancong Lin, Julie Stephany Berrio Perez +1
LiDAR scene flow estimation has settled into a monoculture: nearly all recent methods share the same feed-forward architecture and the same family of self-supervised losses, inheri…
VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow
Yancong Lin, Shiming Wang, Liangliang Nan +2
Scene flow estimation aims to recover per-point motion from two adjacent LiDAR scans. However, in real-world applications such as autonomous driving, points rarely move independent…
Bosch Street Dataset: A Multi-Modal Dataset with Imaging Radar for Automated Driving
Karim Armanious, Maurice Quach, Michael Ulrich +25
This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance syste…
ICP-Flow: LiDAR Scene Flow Estimation with ICP
Yancong Lin, Holger Caesar
Scene flow characterizes the 3D motion between two LiDAR scans captured by an autonomous vehicle at nearby timesteps. Prevalent methods consider scene flow as point-wise unconstrai…
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentation
Jiarong Wei, Yancong Lin, Holger Caesar
Active learning strives to reduce the need for costly data annotation, by repeatedly querying an annotator to label the most informative samples from a pool of unlabeled data, and…