Frustum VoxNet (Depth only)

Reported on 5 benchmarks across 5 tasks · 1 paper

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· 2019-10-12
42.8
best: 58.1 (YONOD + CPPM (RGB + Depth))
Frustum VoxNet for 3D object detection from RGB-D or Depth images arXiv:1910.05483
2D ClassificationonSUN RGB-D
AP 0.5· 2019-10-12
42.8
best: 58.1 (YONOD + CPPM (RGB + Depth))
Frustum VoxNet for 3D object detection from RGB-D or Depth images arXiv:1910.05483
2D Object DetectiononSUN RGB-D
AP 0.5· 2019-10-12
42.8
best: 58.1 (YONOD + CPPM (RGB + Depth))
Frustum VoxNet for 3D object detection from RGB-D or Depth images arXiv:1910.05483
16konSUN RGB-D
AP 0.5· 2019-10-12
42.8
best: 58.1 (YONOD + CPPM (RGB + Depth))
Frustum VoxNet for 3D object detection from RGB-D or Depth images arXiv:1910.05483

Computer Vision1 result

Object DetectiononSUN RGB-D
AP 0.5· 2019-10-12
42.8
best: 58.1 (YONOD + CPPM (RGB + Depth))
Frustum VoxNet for 3D object detection from RGB-D or Depth images arXiv:1910.05483