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 |
– |