Pith. sign in

REVIEW 1 cited by

CARLA-BSP: a simulated dataset with pedestrians

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2305.00204 v1 pith:P7SXICNK submitted 2023-04-29 cs.CV

CARLA-BSP: a simulated dataset with pedestrians

classification cs.CV
keywords datasetframeworkpedestriansposearcaneautoencodingbaselinecarla
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestrian detection, autoencoding, pose estimation, and pose lifting. We also showcase baseline results. For more information, visit https://project-arcane.eu/.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CarlaNCAP: A Framework for Quantifying the Safety of Vulnerable Road Users in Infrastructure-Assisted Collective Perception Using EuroNCAP Scenarios

    cs.RO 2025-12 unverdicted novelty 5.0

    CarlaNCAP framework and 11k-frame dataset show infrastructure collective perception achieves up to 100% accident avoidance in EuroNCAP scenarios versus 33% for vehicle-only sensors.