PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume. Cast in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow estimate to warp the CNN features of the second image. It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in size and easier to train than the recent FlowNet2 model. Moreover, it outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436) images. Our models are available on https://github.com/NVlabs/PWC-Net.Comment: CVPR 2018 camera ready version (with github link to Caffe and PyTorch code

A Fusion Approach for Multi-Frame Optical Flow Estimation

To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has not yet produced state-of-the-art results. We present a simple, yet effective fusion approach for multi-frame optical flow that benefits from longer-term temporal cues. Our method first warps the optical flow from previous frames to the current, thereby yielding multiple plausible estimates. It then fuses the complementary information carried by these estimates into a new optical flow field. At the time of writing, our method ranks first among published results in the MPI Sintel and KITTI 2015 benchmarks. Our models will be available on https://github.com/NVlabs/PWC-Net.Comment: Work accepted at IEEE Winter Conference on Applications of Computer Vision (WACV 2019

Finite Volume Numerical Analysis of the Thermal Property of Cellular Concrete Based on Two and Three Dimensional X-ray Computerized Tomography Images

Cellular concrete is one kind of lightweight concrete, which are widely used in thermal insulation engineering project. In this study, a three dimensional (2D and 3D) finite-volume-based models was developed for analyzing the heat transfer mechanisms through the porous structures of cellular concretes under steady-state heat transfer condition and also for investigating the differences between 2D and 3D modeling results. 2D and 3D reconstructed pore networks were generated from the microstructural information measured by a 3D image captured by X-ray computerized tomography (X-CT). In addition, the 3D-computed value of the effective thermal conductivity was found to be in better agreement with the measured value, in comparison with that computed on the basis of 2D cross-sectional images. Finally, the thermal conductivity computed for different porous 3D networks of cellular concretes were compared with those obtained from 2D computations, revealing the differences between 2D and 3D image-based modeling: a correlation was thus derived between the results computed with 3D and 2D models