Nod’s State of Art Monocular Unsupervised Neural Network Depth estimation outperforms all published research (as of June 2020) and outperforms active solutions such as Azure Kinect DK and Intel RealSense D435i.
|
Methods |
Supervision |
Accuracy Metric (higher is better) |
Error Metric (lower is better) |
|||
|
δ < 1.25 |
δ < 1.25^2 |
δ < 1.25^2 |
Absolute Relative Error |
Root Mean Square Error |
||
|
DORN [1] |
Y |
0.828 |
0.965 |
0.992 |
0.115 |
0.509 |
|
DepthNet Nano [2] |
Y |
0.816 |
0.958 |
0.989 |
0.139 |
0.599 |
|
Zhou et al. [3] |
N |
0.674 |
0.9 |
0.968 |
0.208 |
0.712 |
|
TrainFlow [4] |
N |
0.701 |
0.912 |
0.978 |
0.189 |
0.686 |
|
Nod Depth |
N |
0.7266 |
0.9332 |
0.9794 |
0.1778 |
0.5841 |
|
Nod Depth + Post Process |
N |
0.7419 |
0.947 |
0.988 |
0.1702 |
0.5508 |
|
Nod Depth on DPU (Post Quantization) |
N |
0.701 |
0.9294 |
0.984 |
0.1958 |
0.5854 |
|
Nod Depth on TPU (Post Quantization) |
N |
0.7018 |
0.9244 |
0.9832 |
0.195 |
0.6062 |
[1]:Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao. Deep ordinal regression network for monocular depth estimation. In CVPR, pages 2002–2011, 2018.
[2]:Linda Wang, Mahmoud Famouri, and Alexander Wong. DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation. In CVPR, 2020.
[3]:Junsheng Zhou, Yuwang Wang, Kaihuai Qin, and Wenjun Zeng. Moving indoor: Unsupervised video depth learning in challenging environments. In ICCV, pages 8618–8627, 2019.
[4]:Wang Zhao Shaohui Liu Yezhi Shu Yong-Jin Liu. Towards Better Generalization: Joint Depth-Pose Learning without PoseNet. In CVPR, 2020.
|
Resolution |
GPU |
CPU |
DPU |
TPU |
|
320*240 |
192fps |
23fps |
90fps |
50fps |
|
640*480 |
90fps |
10fps |
25fps |
– |