Aggregated Deep Convolutional Neural Networks for MultiView 3D Object Retrieval Extracting and aggregating discriminative image features is a key challenge for 3D multiview object recognition and retrieval tasks. In this paper, we propose aggregated deep CNNs (ADCNN) model to address the limitations associated with projecting 3D models into multiple 2D images and their resulting highdimensional representations. A deep learning network is developed to aggregate compact features of 3D objects using the activation kernels of convolutional layers directly. Two instances of the same CNN features extractor share the learning weights while they represent different object characteristics. Systematic experiments conducted on the benchmark dataset ModelNet40 demonstrate the efficacy of the proposed method in 3D object retrieval and a m AP accuracy of 91.1% is achieved, which shows its performance superiority over related stateoftheart methods.

Triplanar convolution with shared 2D kernels for 3D classification and shape retrieval Abstract   Increasing the depth of Convolutional Neural Networks (CNNs) has been recognized to provide better generalization performance. However, in the case of 3D CNNs, stacking layers increases the number of learnable parameters linearly, making it more prone to learn redundant features. In this paper, we propose a novel 3D CNN structure that learns shared 2D triplanar features viewed from the three orthogonal planes, which we term S3PNet. Due to the reduced dimension of the convolutions, the proposed S3PNet is able to learn 3D representations with substantially fewer learnable parameters. Experimental evaluations show that the combination of 2D representations on the different orthogonal views learned through the S3PNet is sufficient and effective for 3D representation, with the results outperforming current methods based on fully 3D CNNs. We support this with extensive evaluations on widely used 3D data sources in computer vision: CAD models, LiDAR point clouds, RGBD images, and 3D Computed Tomography scans. Experiments further demonstrate that S3PNet has better generalization capability for smaller training sets, and learns more of kernels with less redundancy compared to kernels learned from 3D CNNs.