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