RGB-D RCNN (RGB + Depth)

Reported on 5 benchmarks across 5 tasks · 1 paper · 5 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology4 results

3DonSUN RGB-D
AP 0.5· 2014-07-22
44.2
best: 58.1 (YONOD + CPPM (RGB + Depth))
SOTA
Learning Rich Features from RGB-D Images for Object Detection and Segmentation arXiv:1407.5736
2D ClassificationonSUN RGB-D
AP 0.5· 2014-07-22
44.2
best: 58.1 (YONOD + CPPM (RGB + Depth))
SOTA
Learning Rich Features from RGB-D Images for Object Detection and Segmentation arXiv:1407.5736
2D Object DetectiononSUN RGB-D
AP 0.5· 2014-07-22
44.2
best: 58.1 (YONOD + CPPM (RGB + Depth))
SOTA
Learning Rich Features from RGB-D Images for Object Detection and Segmentation arXiv:1407.5736
16konSUN RGB-D
AP 0.5· 2014-07-22
44.2
best: 58.1 (YONOD + CPPM (RGB + Depth))
SOTA
Learning Rich Features from RGB-D Images for Object Detection and Segmentation arXiv:1407.5736

Computer Vision1 result

Object DetectiononSUN RGB-D
AP 0.5· 2014-07-22
44.2
best: 58.1 (YONOD + CPPM (RGB + Depth))
SOTA
Learning Rich Features from RGB-D Images for Object Detection and Segmentation arXiv:1407.5736