Submission: Extended FCOS+PFA-MixPBR-RGB/TUD-L

Submission name
Submission time (UTC) Oct. 3, 2022, 7:43 a.m.
User Yang-hai
Task Pose estimation (BOP 2019-2022)
Dataset TUD-L
Training model type Default
Training image type Synthetic + real
Description
Evaluation scores
AR:0.839
AR_MSPD:0.978
AR_MSSD:0.820
AR_VSD:0.719
average_time_per_image:0.866

Method: Extended FCOS+PFA-MixPBR-RGB

User Yang-hai
Publication Yinlin Hu et, at: Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimation, ECCV, 2022
Implementation
Training image modalities RGB
Test image modalities RGB
Description

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 + Real (if available)

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