Smoke density estimation from a single image is a totally new but highly ill-posed problem. To solve the problem, we stack several convolutional encoder-decoder structures together to propose a wave-shaped neural network, termed W-Net. Stacking encoder-decoders directly increases the network depth, leading to the enlargement of receptive fields for encoding more semantic information. To maximize the degrees of feature re-usage, we copy and resize the outputs of encoding layers to corresponding decoding layers, and then concatenate them to implement short-cut connections for improving spatial accuracy. The crests and troughs of W-Net are special structures containing abundant localization and semantic information, so we also use short-cut connections between these structures and decoding layers. Estimated smoke density is useful in many applications, such as smoke segmentation, smoke detection, disaster simulation. Experimental results show that our method outperforms existing methods on both smoke density estimation and segmentation. It also achieves satisfying results in visual detection of auto exhausts.