BOP: Benchmark for 6D Object Pose Estimation

Method: RACE6D_RGB

User	helose
Publication	RACE-6D: Real-time Accurate Coarse-to-finE object 6D Pose Transformer, CVPR 2026 Findings
Implementation	Pytorch, code can be found at https://github.com/Yoonwoo-Ha/RACE-6D
Views	Single
Test image modalities	RGB
Description	Training data: real + provided PBR Used 3D models: Default for other datasets Authors: Yoonwoo Ha, Hyungpil Moon (SungKyunKwan University). For LMO, HB, ICBIN datasets, we only use the provided synthetic training data (PBR) in training. While for YCBV, TUDL, TLESS, we use the provided real data and synthetic data (PBR) in training. For detection, we developed a unified pose estimation model that encompasses the object detection process
Computer specifications	GPU RTX 3090; CPU intel i9-12900K