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arXiv · 2609.08607

GOLF: Global Observation with Local Focus for Calibration-Aware Stereo Interaction Field Estimation

Minqiang Zou·Riqiang Jin·Zhi Lv·Dong Luo·Lianghai Tian·Zhenyu Zhao·Qi Xu·Tong Wu·Mochen Yu·Yao Tang
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Abstract

We present GOLF, the first-place solution to the SHOW3D Interaction Field Estimation Challenge at HANDS@ECCV 2026. Given synchronized egocentric stereo views, the task is to predict a 3D vector from each of 21 hand joints to the closest point on the manipulated object. GOLF combines dense global context, locally sampled hand/object evidence, and common-frame Pl\"ucker-ray geometry. We adapt DINOv3 ViT-H+/16 with LoRA and trainable LayerNorm parameters, then jointly decode both interaction fields. Our primary model achieves an official score of 27.61 and a mean ADE of 27.96 mm on the hidden test set. An equal-weight ensemble with a complementary directly fine-tuned variant improves these results to an official score of 27.47 and a mean ADE of 27.82 mm, securing first place.

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Minqiang Zou, Riqiang Jin, Zhi Lv, Dong Luo, Lianghai Tian, Zhenyu Zhao, Qi Xu, Tong Wu, Mochen Yu, Yao Tang. 2026-09-08. GOLF: Global Observation with Local Focus for Calibration-Aware Stereo Interaction Field Estimation. https://arxiv.org/abs/2609.08607

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