DeepAI AI Chat
Log In Sign Up

Depth Reconstruction of Translucent Objects from a Single Time-of-Flight Camera using Deep Residual Networks

by   Seongjong Song, et al.

We propose a novel approach to recovering the translucent objects from a single time-of-flight (ToF) depth camera using deep residual networks. When recording the translucent objects using the ToF depth camera, their depth values are severely contaminated due to complex light interactions with the surrounding environment. While existing methods suggested new capture systems or developed the depth distortion models, their solutions were less practical because of strict assumptions or heavy computational complexity. In this paper, we adopt the deep residual networks for modeling the ToF depth distortion caused by translucency. To fully utilize both the local and semantic information of objects, multi-scale patches are used to predict the depth value. Based on the quantitative and qualitative evaluation on our benchmark database, we show the effectiveness and robustness of the proposed algorithm.


page 6

page 9

page 10

page 11

page 12


Structure-Aware Residual Pyramid Network for Monocular Depth Estimation

Monocular depth estimation is an essential task for scene understanding....

Matching-based Depth Camera and Mirrors for 3D Reconstruction

Reconstructing 3D object models is playing an important role in many app...

Learning the Depths of Moving People by Watching Frozen People

We present a method for predicting dense depth in scenarios where both a...

MSFNet:Multi-scale features network for monocular depth estimation

In recent years, monocular depth estimation is applied to understand the...

Novel View Synthesis for Large-scale Scene using Adversarial Loss

Novel view synthesis aims to synthesize new images from different viewpo...

Rate-Distortion Analysis of Multiview Coding in a DIBR Framework

Depth image based rendering techniques for multiview applications have b...

Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo

We propose a computational model for shape, illumination and albedo infe...