In: IEEE international conference on computer vision (ICCV), p 858–865. Hinterstoisser S, Holzer S, Cagniart C, Ilic S, Konolige K, Navab N, Lepetit V (2011) Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: IEEE conference on computer vision and pattern recognition (CVPR), p 770–778. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE international conference on image processing (ICIP), pp 2641–2645. Hagelskjær F, Buch AG (2020) Pointvotenet: accurate object detection and 6 DOF pose estimation in point clouds. In: European conference on computer vision (ECCV), p 345–360. Gupta S, Girshick R, Arbeláez P, Malik J (2014)Learning rich features from RGB-D images for object detection and segmentation. Guo J, Xing X, Quan W, Yan D-M, Gu Q, Liu Y, Zhang X (2021) Efficient center voting for object detection and 6D pose estimation in 3D point cloud. In: IEEE international conference on robotics and automation (ICRA), p 3643–3649. Gao G, Lauri M, Wang Y, Hu X, Zhang J, Frintrop S (2020) 6d object pose regression via supervised learning on point clouds. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), p 681–687. Įitel A, Springenberg JT, Spinello L, Riedmiller M, Burgard W (2015) Multimodal deep learning for robust RGB-D object recognition. ĭu G, Wang K, Lian S, Zhao K (2021) Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review. In: IEEE winter conference on applications of computer vision (WACV), p 2813–2822. Ĭhen W, Duan J, Basevi H, Chang HJ, Leonardis A (2020) PointPoseNet: point pose network for robust 6d object pose estimation. In: IEEE/CVF conference on computer vision and pattern recognition (CVPR), p 652–660. Ĭharles RQ, Su H, Kaichun M, Guibas LJ (2017) PointNet: deep learning on point sets for 3D classification and segmentation. In: IEEE international conference on robotics and automation (ICRA), pp 6140–6146. īui M, Zakharov S, Albarqouni S, Ilic S, Navab N (2018) When regression meets manifold learning for object recognition and pose estimation. In: Sensor fusion IV: control paradigms and data structures, vol 1611, p 586–606. īesl PJ, McKay ND (1992) Method for registration of 3-D shapes. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 3762–3769. Experiments on the LINEMOD and YCB-Video datasets show that our EFN6D outperforms state-of-the-art methods by a large margin.Īubry M, Maturana D, Efros AA, Russell BC, Sivic J (2014) Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of cad models. Finally, the fused features obtained from these two ResNets and the point cloud features are densely fused point by point to further strengthen the fusion of 2D and 3D information at a per-pixel level. Besides, the PSP modules and skip connections are used between the two ResNets, which not only enhances cross modal fusion performance of the network but also enhances the network’s capability in handling objects at different scales. To effectively fuse the surface texture features and the geometric contour features of the object, we feed the RGB images and the normal map into two ResNets. Instead of directly using the original single-channel depth map, we encode the depth information into a normal map and point cloud data. Therefore, we propose an efficient RGB-D fusion network for 6D pose estimation, called EFN6D, to exploit the 2D–3D feature more thoroughly. Most existing methods concatenate these two data sources directly, which does not make full use of their complementary relationship. With the wide use of RGB-D cameras, we can directly capture both the depth for the object relative to the camera and the corresponding RGB image. Lacking depth information, traditional pose estimators using only RGB cameras consistently predict bias 3D rotation and translation matrices. Precise 6DoF (6D) object pose estimation is an essential topic for many intelligent applications, for example, robot grasping, virtual reality, and autonomous driving.
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