RESEARCH

COMPUTER VISION

DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs

September 9, 2018

Abstract

Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest iPhone X. However, they still suffer from heavy noises which dramatically limit their applications. Although plenty of progresses have been made to reduce the noises and boost geometric details, due to the inherent illness and the real-time requirement, the problem is still far from been solved. We propose a cascaded Depth Denoising and Refinement Network (DDRNet) to tackle this problem by leveraging the multi-frame fused geometry and the accompanying high quality color image through a joint training strategy. The classic rendering equation is delicately exploited in our network in an unsupervised manner. Experimental results indicate that our network achieves real-time denoising and refinement on various categories of static and dynamic scenes. Thanks to the well decoupling of the low and high frequency information in the cascaded network, we achieve superior performance over the state-of-the-art techniques.

Download the Paper

Related Publications

May 17, 2019

COMPUTER VISION

SPEECH & AUDIO

GLoMo: Unsupervised Learning of Transferable Relational Graphs | Facebook AI Research

Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However,…

Zhilin Yang, Jake (Junbo) Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun

May 17, 2019

May 06, 2019

COMPUTER VISION

NLP

No Training Required: Exploring Random Encoders for Sentence Classification | Facebook AI Research

We explore various methods for computing sentence representations from pre-trained word embeddings without any training, i.e., using nothing but random parameterizations. Our aim is to put sentence embeddings on more solid footing by 1) looking…

John Wieting, Douwe Kiela

May 06, 2019

May 06, 2019

NLP

COMPUTER VISION

Efficient Lifelong Learning with A-GEM | Facebook AI Research

In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task. In this work, we investigate the efficiency of current lifelong…

Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach, Mohamed Elhoseiny

May 06, 2019

May 06, 2019

COMPUTER VISION

Learning Exploration Policies for Navigation | Facebook AI Research

Numerous past works have tackled the problem of task-driven navigation. But, how to effectively explore a new environment to enable a variety of down-stream tasks has received much less attention. In this work, we study how agents can…

Tao Chen, Saurabh Gupta, Abhinav Gupta

May 06, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.