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Submission: SurfEmb-PBR-RGBD/LM-O

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Submission name
Submission time (UTC) Dec. 22, 2021, 5:46 p.m.
User surfemb
Task Model-based 6D localization of seen objects
Dataset LM-O
Training model type Default
Training image type Synthetic (only PBR images provided for BOP Challenge 2020 were used)
Description
Evaluation scores
AR:0.760
AR_MSPD:0.856
AR_MSSD:0.809
AR_VSD:0.615
average_time_per_image:11.943

Method: SurfEmb-PBR-RGBD

User surfemb
Publication Rasmus Laurvig Haugaard, Anders Glent Buch: SurfEmb, CVPR 2022
Implementation https://github.com/rasmushaugaard/surfemb
Training image modalities RGB
Test image modalities RGB-D
Description

SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings

Project site
Pre-print

Compared to the paper:

  • All models are trained for 500k iterations
  • 10k pose samples instead of 20k, and maximum 1k pose evaluations after pruning.

We use the available detections from CosyPose, but were not able to find detection-only timings. We add our timing on top of the full CosyPose single-view RGB pipeline, resulting in conservative timings.
Computer specifications 1 x RTX 2080