3D on SUN RGB-D

Metric: AP 0.5 (higher is better)

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#	Model↕	AP 0.5▼	Augmentations	Paper	Date↕	Code
1	YONOD + CPPM (RGB + Depth)	58.1	No	Unified Object Detector for Different Modalities...	2022-07-03	Code
2	Frustum Pointnet (RGB)	56.8	No	Frustum PointNets for 3D Object Detection from R...	2017-11-22	Code
3	simCrossTrans with Swin Transformer (Point Cloud only)	55.8	No	simCrossTrans: A Simple Cross-Modality Transfer ...	2022-03-20	Code
4	2D - Driven (RGB)	49.7	No	-	-	-
5	Frustum VoxNet (RGB)	47.9	No	Frustum VoxNet for 3D object detection from RGB-...	2019-10-12	-
6	RGB-D RCNN (RGB + Depth)	44.2	No	Learning Rich Features from RGB-D Images for Obj...	2014-07-22	Code
7	Frustum VoxNet (Depth only)	42.8	No	Frustum VoxNet for 3D object detection from RGB-...	2019-10-12	-

#1YONOD + CPPM (RGB + Depth)SOTA
58.1
AP 0.5· 2022-07-03
Unified Object Detector for Different Modalities based on Vision Transformers Code
#2Frustum Pointnet (RGB)SOTA
56.8
AP 0.5· 2017-11-22
Frustum PointNets for 3D Object Detection from RGB-D Data Code
#3simCrossTrans with Swin Transformer (Point Cloud only)
55.8
AP 0.5· 2022-03-20
simCrossTrans: A Simple Cross-Modality Transfer Learning for Object Detection with ConvNets or Vision Transformers Code
#42D - Driven (RGB)
49.7
AP 0.5
No paper
#5Frustum VoxNet (RGB)
47.9
AP 0.5· 2019-10-12
Frustum VoxNet for 3D object detection from RGB-D or Depth images
#6RGB-D RCNN (RGB + Depth)SOTA
44.2
AP 0.5· 2014-07-22
Learning Rich Features from RGB-D Images for Object Detection and Segmentation Code
#7Frustum VoxNet (Depth only)
42.8
AP 0.5· 2019-10-12
Frustum VoxNet for 3D object detection from RGB-D or Depth images