A Lightweight Neural Network for Monocular View Generation with Occlusion Handling

TL;DR

This paper introduces a lightweight neural network that synthesizes novel views from a single image using disparity estimation and occlusion handling, outperforming state-of-the-art methods on KITTI with significantly fewer parameters.

Contribution

A novel, efficient neural network architecture for monocular view synthesis that incorporates occlusion handling and disparity consistency, reducing model size while maintaining high performance.

Findings

Outperforms state-of-the-art on KITTI dataset.

Reduces model parameters by 5-10 times.

Generates accurate depth and confidence maps.

Abstract

In this article, we present a very lightweight neural network architecture, trained on stereo data pairs, which performs view synthesis from one single image. With the growing success of multi-view formats, this problem is indeed increasingly relevant. The network returns a prediction built from disparity estimation, which fills in wrongly predicted regions using a occlusion handling technique. To do so, during training, the network learns to estimate the left-right consistency structural constraint on the pair of stereo input images, to be able to replicate it at test time from one single image. The method is built upon the idea of blending two predictions: a prediction based on disparity estimation, and a prediction based on direct minimization in occluded regions. The network is also able to identify these occluded areas at training and at test time by checking the pixelwise left-right consistency of the produced disparity maps. At test time, the approach can thus generate a left-side and a right-side view from one input image, as well as a depth map and a pixelwise confidence measure in the prediction. The work outperforms visually and metric-wise state-of-the-art approaches on the challenging KITTI dataset, all while reducing by a very significant order of magnitude (5 or 10 times) the required number of parameters (6.5 M).