Submission: Drost-CVPR10-3D-Only/TYO-L

Submission name
Submission time (UTC) July 31, 2019, 5:08 a.m.
User Berti
Task Pose estimation (BOP 2019-2022)
Dataset TYO-L
Training model type Default
Training image type None
Description
Evaluation scores
AR:0.565
AR_MSPD:0.546
AR_MSSD:0.548
AR_VSD:0.600
average_time_per_image:5.599

Method: Drost-CVPR10-3D-Only

User Berti
Publication Drost et al., Model globally, match locally: Efficient and robust 3D object recognition, CVPR 2010
Implementation
Training image modalities None
Test image modalities D
Description

An implementation of the paper, including ICP for post-processing.

The parameters are set to a slower, but more accurate mode (relative sampling distance of 0.03 for model creation and matching).

No images are used. CPU only, no GPU is used. The training is done using the CAD models only.

Implementation: HALCON 19.05 progress.

Computer specifications Max. 12 Threads on intel Xeon CPU E5-2690 v4 @ 2.60GHz, 2601 Mhz