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SUN RGB-D

Benchmarks

Task NameDataset NameSOTA ResultTrend
Semantic SegmentationSUN RGB-D (test)
mIoU54.6
191
3D Object DetectionSUN RGB-D (val)
mAP@0.2569.7
158
3D Object DetectionSUN RGB-D
mAP@0.2567.9
104
Depth EstimationSUN RGB-D (test)
Root Mean Square Error (RMS)0.275
93
3D Object DetectionSUN RGB-D v1 (val)
mAP@0.2568.9
81
3D Object DetectionSUN RGB-D (test)
mAP@0.2567.4
64
Semantic SegmentationSUN RGB-D
mIoU53
45
Scene RecognitionSUN RGB-D Scene (test)
Acc (RGB-D)60.7
25
Multi-modal RecognitionSUN RGB-D
Accuracy0.5807
24
Monocular Depth EstimationSUN RGB-D
Absolute Relative Error (Abs Rel)0.085
19
Indoor Object DetectionSUN RGB-D (test)
mAP@0.547.5
19
Depth EstimationSUN RGB-D
Depth Error0.386
18
Depth CompletionSUN RGB-D (test)
RMSE0.214
18
3D Object DetectionSUN RGB-D v1 (test)
Bed AP82.9
18
Monocular Depth EstimationSUN RGB-D v1 (test)
Delta-1 Acc93.7
14
3D Layout EstimationSUN RGB-D
IoU64.4
14
3D Spatial GroundingSUN RGB-D
AP1548.3
10
Semantic SegmentationSUN RGB-D (val)
Wall mIoU74.99
9
Camera Pose EstimationSUN RGB-D
Pitch2.63
9
Object DetectionSUN RGB-D (test)
mAP19.1
8
3D Layout EstimationSUN RGB-D v1 (test)
Average IoU66.1
8
Depth EstimationSUN RGB-D unseen indoor scenes
δ1 Accuracy86.6
7
3D Object RecognitionSUN RGB-D (common classes split)
Top-1 Accuracy (Average)69.6
6
Scene ClassificationSUN RGB-D
Accuracy64.9
6
3D Object DetectionTraditional SUN RGB-D
AP@0.2568.8
6
Showing 25 of 56 rows