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GOLF system wins SHOW3D Interaction Field Estimation Challenge

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GOLF, a novel system that achieved first place in the SHOW3D Interaction Field Estimation Challenge at HANDS@ECCV 2026. GOLF utilizes synchronized stereo views to predict 3D vectors from hand joints to the closest object points. The system integrates dense global context with local hand and object evidence, employing a modified DINOv3 ViT-H+/16 model enhanced with LoRA+ and trainable LayerNorm parameters. AI

IMPACT This research advances stereo interaction field estimation, potentially improving human-computer interaction and robotics applications.

RANK_REASON The cluster describes a research paper detailing a system that won a specific challenge. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GOLF system wins SHOW3D Interaction Field Estimation Challenge

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The cluster describes a research paper detailing a system that won a specific challenge. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Minqiang Zou, Riqiang Jin, Zhi Lv, Dong Luo, Lianghai Tian, Zhenyu Zhao, Qi Xu, Tong Wu, Mochen Yu, Yao Tang ·

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

    arXiv:2609.08607v2 Announce Type: replace 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…

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