SOTAVerified

6D Pose Estimation

Image: Zeng et al

Papers

Showing 176–200 of 255 papers

TitleStatusHype
A Multi-body Tracking Framework -- From Rigid Objects to Kinematic StructuresCode0
MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Network—0
Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset—0
Unseen Object 6D Pose Estimation: A Benchmark and Baselines—0
Category-Agnostic 6D Pose Estimation with Conditional Neural Processes—0
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions—0
DeepRM: Deep Recurrent Matching for 6D Pose Refinement—0
YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression—0
DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation—0
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation—0
Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation—0
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation—0
Sim2Real Instance-Level Style Transfer for 6D Pose Estimation—0
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects—0
Adversarial samples for deep monocular 6D object pose estimationCode0
Sim2Real Object-Centric Keypoint Detection and Description—0
Towards Deep Learning-based 6D Bin Pose Estimation in 3D ScansCode0
SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings—0
MPF6D: Masked Pyramid Fusion 6D Pose Estimation—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
Differentiable Rendering with Perturbed Optimizers—0
T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression—0
PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation—0
Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks—0
Keypoint-Graph-Driven Learning Framework for Object Pose Estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICG+ADDS AUC97.9—Unverified
2FFB6DADDS AUC96.6—Unverified
3ICGADDS AUC96.5—Unverified
4PVN3DADDS AUC96.1—Unverified
5se3-TrackNetADDS AUC95.71—Unverified
6CMCL6DADDS AUC95.43—Unverified
7DTTD-Net w/o refinerADDS AUC94.19—Unverified
8MaskedFusionADDS AUC93.3—Unverified
9DenseFusionADDS AUC93.1—Unverified
10PoseCNN+ICPADDS AUC93—Unverified
#ModelMetricClaimedVerifiedStatus
16D OBJECT POSE TRACKING IN INTERNET VIDEOS FOR ROBOTIC MANIPULATIONAR CoU52.1—Unverified
2MegaPose-RGBD (refined)ADD AUC49.02—Unverified
3GigaPoseAR CoU39.32—Unverified
4FoundPoseAR CoU31.32—Unverified
5MegaPose-RGBD (Coarse)AR CoU13.72—Unverified
#ModelMetricClaimedVerifiedStatus
1FFB6DAccuracy (ADD)99.7—Unverified
2PVN3DAccuracy (ADD)99.4—Unverified
3GPV-PoseMean ADD-S98.2—Unverified
4MaskedFusionAccuracy (ADD)97.8—Unverified
5DenseFusionAccuracy (ADD)94.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ICG+AUC17.57—Unverified
2ICGAUC16.54—Unverified
#ModelMetricClaimedVerifiedStatus
1VISAPP BaselineeRE0.2—Unverified