Submission: RADet+PFA-PBR-RGBD/HB

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Submission name
Submission time (UTC) Oct. 8, 2022, 10:57 a.m.
User Yang-hai
Task 6D localization of seen objects
Dataset HB
Training model type Default
Training image type Synthetic (only PBR images provided for BOP Challenge 2020 were used)
Evaluation scores


User Yang-hai
Publication Yang Hai et, al; Rigidity-Aware Detection for 6D Object Pose Estimation; Yinlin Hu et, at: Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimation, ECCV, 2022
Training image modalities RGB
Test image modalities RGB-D

We train a single model for all objects on each dataset, and based on an architecture of object detection and pose regression.

Object detection: extended FCOS

Pose regression: extended PFA-Pose

Data: PBR

RGBD track: the same models used in RGB Track, RANSAC-Kabsch for depth utilizing.

The Main differences from FCOS:

  1. We use stronger augmentations following the best practice in 6D pose estimation

  2. We utilize some mask information for a better sampling of positive signals during training

The main differences from the original PFA-Pose paper:

  • It does not use a detection component, we embed a detection component into it to facilitate the pose regression.

  • It uses exemplars rendered offline for training, which is resource-friendly and efficient during training. For this competition, we replace it with online rendering to achieve better accuracy.

  • It uses only flow from the rendered image to the input. We further use a backward flow from the input to the rendered image for a consistent check to remove more outliers.

List of contributors:

Yang Hai, Rui Song, Zhiqiang Liu, Jiaojiao Li (Xidian University)

Mathieu Salzmann, Pascal Fua (EPFL)

Yinlin Hu (Magic Leap)

Computer specifications NVIDIA 3090