6d Object Pose Regression Via Supervised Learning On Point Clouds Icra2020 Presentation

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Information ICRA 2020: Robust 6D Object Pose Estimation by Learning RGB-D Features Guide

Stay updated on 6d Object Pose Regression Via Supervised Learning On Point Clouds Icra2020 Presentation's newest achievements.

HybridPose: 6D Object Pose Estimation Under Hybrid Representations

Robust 6D Object Pose Estimation in Cluttered Scenes

Self6D: Self-Supervised Monocular 6D Object Pose Estimation. ECCV 2020 (oral)

Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge

Generating Annotated Training Data for 6D Object Pose Estimation in Operational Environments

Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation

From Image Collections to Point Clouds With Self-Supervised Shape and Pose Networks

CVPR 2020: Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels

Improving 6D Pose Estimation of Objects in Clutter Via Physics-Aware Monte Carlo Tree Search

Automatic and Reatime 6D Pose Estimation for Inspection Planning Purposes

Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation

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Last Updated: October 4, 2026

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Information [ICRA 2020] 6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints News

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6D Object Pose Regression via Supervised Learning Paper: arxiv.org/abs/2003.00188 Source code: github.com/mentian/ Authors: Chen Song, Jiaru Song, Qixing Huang Description: We introduce HybridPose, a novel Video attachement for: Arul Selvam Periyasamy, Max Schwarz, and Sven Behnke: "Robust Supplementary video submission. Project website: cs.princeton.edu/~andyz/apc2016 Abstract: Warehouse automation ... This is a complementary video for the preprint "Generating Annotated Training Data for Title: Pix2Pose: Pixel-Wise Coordinate Authors: K L Navaneet, Ansu Mathew, Shashank Kashyap, Wei-Chih Hung, Varun Jampani, R. Venkatesh Babu Description: ... Authors: Dengsheng Chen, Jun Li, Zheng Wang, Kai Xu Description: We present a novel approach to category-level

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