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Byeongjun Kwon

2 accepted papers

2025

One Look is Enough: Seamless Patchwise Refinement for Zero-Shot Monocular Depth Estimation on High-Resolution Images

ICCV 2025poster

Zero-shot depth estimation (DE) models exhibit strong generalization performance as they are trained on large-scale datasets. However, existing models struggle with high-resolution images due to the discrepancy in image resolutions of training (with smaller resolutions) and inference (for high resol…

Cited by 0SourcePDFScholar
2024

From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Prior

CVPR 2024poster

Self-supervised monocular depth estimation (DE) is an approach to learning depth without costly depth ground truths. However it often struggles with moving objects that violate the static scene assumption during training. To address this issue we introduce a coarse-to-fine training strategy leveragi…

Cited by 12SourcePDFScholar