computer-vision-7

6D Pose Estimation using RGBD

3260 papers • 126 benchmarks • 313 datasets

Image: Zeng et al

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Benchmarks

These leaderboards are used to track progress in 6d-pose-estimation-using-rgbd-7

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Dataset

Best Model

Actions

REAL275

LineMOD

YCB-Video

Libraries

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Use these libraries to find 6d-pose-estimation-using-rgbd-7 models and implementations

dlr-rm/3dobjecttracking

2 papers 710

Datasets

LM

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No subtasks available.

Most implemented papers

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

D. Fox, Yu Xiang, Tanner Schmidt, V. Narayanan•Tue Oct 31 2017

This work introduces PoseCNN, a new Convolutional Neural Network for 6D object pose estimation, which is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input.

2186

Content

CAMERA25

OccludedLINEMOD

T-LESS

Tejani

Drunkard's Dataset Level3 Scene0

thodan/bop_toolkit

2 papers 400

6IMPOSE

Drunkard's Dataset

0

Paper Graph

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

S. Savarese, Li Fei-Fei, Yuke Zhu, Roberto Martín-Martín, Chen Wang, Danfei Xu, Cewu Lu•Mon Jan 14 2019

DenseFusion is a generic framework for estimating 6D pose of a set of known objects from RGB-D images that processes the two data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated.

1092 0

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BOP Challenge 2020 on 6D Object Localization

Eric Brachmann, Frank Michel, C. Rother, Jiri Matas, Tomás Hodan, M. Sundermeyer, Bertram Drost, Yann Labbé•Mon Sep 14 2020

Although the top-performing methods rely on RGB-D image channels, strong results were achieved when only RGB channels were used at both training and test time, and the photorealism of PBR images was demonstrated effective despite the augmentation.

312 0

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The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints

Gregory Hager, Andrew Hundt, Varun Jain, Chia-Hung Lin, Chris Paxton•Fri Oct 26 2018

It is shown that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances.

12 0

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PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation

Yisheng He, Wei Sun, Haibin Huang, Haoqiang Fan, Jian Sun, Jianran Liu•Sun Nov 10 2019

A deep Hough voting network is proposed to detect 3D keypoints of objects and then estimate the 6D pose parameters within a least-squares fitting manner, which is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation.

547 0

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CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation

Z. Kira, Michael Laskey, Muhammad Zubair Irshad, T. Kollar, Kevin Stone•Wed Mar 02 2022

This paper presents a simple one- stage approach to predict both the 3D shape and estimate the 6D pose and size jointly in a bounding-box free manner and significantly outperforms all shape completion and categorical 6D poses and size estimation baselines on multi-object ShapeNet and NOCS datasets respectively.

92 0

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GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting

N. Navab, Xiangyang Ji, Federico Tombari, Fabian Manhardt, Yan Di, Ruida Zhang, Zhiqiang Lou•Mon Mar 14 2022

GPV-Pose is proposed, a novel framework for robust category-level pose estimation, harnessing geometric insights to enhance the learning of category- level pose-sensitive features, which produces superior results to state-of-the-art competitors on common public benchmarks, whilst almost achieving real-time inference speed at 20 FPS.

153 0

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Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

Jianxiong Xiao, Andy Zeng, Shuran Song, Alberto Rodriguez, Kuan-Ting Yu, Daniel Suo, Ed Walker•Wed Sep 28 2016

This paper proposes a self-supervised method to generate a large labeled dataset without tedious manual segmentation and demonstrates that the system can reliably estimate the 6D pose of objects under a variety of scenarios.

477 0

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6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints

S. Savarese, Li Fei-Fei, Yuke Zhu, Roberto Mart'in-Mart'in, Chen Wang, Danfei Xu, Jun Lv, Cewu Lu•Tue Oct 22 2019

6-PACK learns to compactly represent an object by a handful of 3D keypoints, based on which the interframe motion of an object instance can be estimated through keypoint matching, and substantially outperforms existing methods on the NOCS category-level 6D pose estimation benchmark.

167 0

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FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism

A. Leonardis, H. Chang, Jinming Duan, Linlin Shen, Wei Chen, Xi Jia•Thu Mar 11 2021

A fast shape-based network (FS-Net) with efficient category-level feature extraction for 6D pose estimation and a novel decoupled rotation mechanism that employs two decoders to complementarily access the rotation information.

190 0

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