TCD-Arena is a new customizable testing framework that runs millions of experiments to map how 33 different assumption violations affect time series causal discovery methods and shows ensembles can boost overall robustness.
Deep Learning-Based Depth Estimation Methods from Monoc- ular Image and Videos: A Comprehensive Survey
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SS3D pretrains an end-to-end feed-forward 3D estimator on filtered YouTube-8M videos via SfM self-supervision, MVS filtering, and expert distillation, delivering stronger zero-shot transfer and fine-tuning than prior self-supervised baselines.
A framework uses standardized US license plate typography and geometry as passive fiducials for metric monocular distance, velocity, and time-to-collision estimation without machine learning training.
A distilled student policy using monocular depth estimation from cameras outperforms a 2D LiDAR teacher policy in navigating complex 3D obstacles while running fully onboard a Jetson Orin.
citing papers explorer
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TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
TCD-Arena is a new customizable testing framework that runs millions of experiments to map how 33 different assumption violations affect time series causal discovery methods and shows ensembles can boost overall robustness.
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SS3D: End2End Self-Supervised 3D from Web Videos
SS3D pretrains an end-to-end feed-forward 3D estimator on filtered YouTube-8M videos via SfM self-supervision, MVS filtering, and expert distillation, delivering stronger zero-shot transfer and fine-tuning than prior self-supervised baselines.
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Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography
A framework uses standardized US license plate typography and geometry as passive fiducials for metric monocular distance, velocity, and time-to-collision estimation without machine learning training.
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Learning Vision-Based Omnidirectional Navigation: A Teacher-Student Approach Using Monocular Depth Estimation
A distilled student policy using monocular depth estimation from cameras outperforms a 2D LiDAR teacher policy in navigating complex 3D obstacles while running fully onboard a Jetson Orin.