DriveSpatial benchmark shows the best of 15 VLMs trails humans by 28.4 points on spatiotemporal driving tasks, with cognitive scene construction as the main failure mode.
An empirical analysis on spatial reasoning capabilities of large multimodal models.arXiv preprint arXiv:2411.06048, 2024
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VLMs reach only 0.66 accuracy on relative camera pose estimation while humans achieve 0.91 and specialized pipelines reach 0.99, exposing weaknesses in multi-view spatial reasoning.
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DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving
DriveSpatial benchmark shows the best of 15 VLMs trails humans by 28.4 points on spatiotemporal driving tasks, with cognitive scene construction as the main failure mode.
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Lost in Space? Vision-Language Models Struggle with Relative Camera Pose Estimation
VLMs reach only 0.66 accuracy on relative camera pose estimation while humans achieve 0.91 and specialized pipelines reach 0.99, exposing weaknesses in multi-view spatial reasoning.