3D object retrieval with stacked local convolutional autoencoder
It proposes a novel 3D model retrieval method with stacked local convolutional autoencoder (SLCAE). The greedy layerwise strategy is applied to train the SLCAE. A gradient descent method is used for training each layer. The experiments are conducted on three publicly available 3D object datasets, and the results demonstrate that the proposed method can greatly improve 3D object retrieval performance, compared with several state-of-the-art methods.
3d object retrieval stacked local convolutional autoencoder object representation