While 360 cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-trivial. Convolutional neural networks (CNNs) trained on images from perspective cameras yield "flat filters, yet 360 images cannot be projected to a single plane without significant distortion. A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems. We propose to learn a spherical convolutional network that translates a planar CNN to process 360 imagery directly in its equirectangular projection. Our approach learns to reproduce the flat filter outputs on 360 data, sensitive to the varying distortion effects across the viewing sphere. The key benefits are 1) efficient feature extraction for 360 images and video, and 2) the ability to leverage powerful pre-trained networks researchers have carefully honed (together with massive labeled image training sets) for perspective images. We validate our approach compared to several alternative methods in terms of both raw CNN output accuracy as well as applying a state-of-the-art "flat object detector to 360 data. Our method yields the most accurate results while saving orders of magnitude in computation versus the existing exact reprojection solution.

This paper describes a method to transform networks learned on perspective images to take spherical images as input. This is an important problem as fisheye and 360-degree sensors become more and more ubiquitous but training data is relatively scarce. The method first transforms the network architecture to adapt the filter sizes and pooling operations to convolutions on a equirectangular representation/projection. Next the filters are learned to match the feature responses of the original network when considering the projections to the tangent plane of the respective feature response. The filters are pre-learned layer-by-layer and fine-tuned to output features as similar as possible to the original network projected to the tangent planes. Detection experiments on Pano2Vid and PASCAL demonstrate that the technique performs slightly below the optimal performance using per-pixel tangent projections (however significantly faster) while outperforming several baselines, including cube map projections.

Online Class-Incremental continual Learning (OCIL) addresses the challenge of continuously learning from a single-channel data stream, adapting to new tasks while mitigating catastrophic forgetting. Recently, Mutual Information (MI)-based methods have shown promising performance in OCIL. However, existing MI-based methods treat various knowledge components in isolation, ignoring the knowledge confusion across tasks. This narrow focus on simple MI knowledge alignment may lead to old tasks being easily forgotten with the introduction of new tasks, risking the loss of common parts between past and present knowledge.To address this, we analyze the MI relationships from the perspectives of diversity, representativeness, and separability, and propose an Enhanced Mutual Information (EMI) method based on knwoledge decoupling. EMI consists of Diversity Mutual Information (DMI), Representativeness Mutual Information (RMI) and Separability Mutual Information (SMI). DMI diversifies intra-class sample features by considering the similarity relationships among inter-class sample features to enable the network to learn more general knowledge. RMI summarizes representative features for each category and aligns sample features with these representative features, making the intra-class sample distribution more compact. SMI establishes MI relationships for inter-class representative features, enhancing the stability of representative features while increasing the distinction between inter-class representative features, thus creating clear boundaries between class. Extensive experimental results on widely used benchmark datasets demonstrate the superior performance of EMI over state-of-the-art baseline methods.

While 360 cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-trivial. Convolutional neural networks (CNNs) trained on images from perspective cameras yield "flat" filters, yet 360 images cannot be projected to a single plane without significant distortion. A naive solution that repeatedly projects the viewing sphere to all tangent planes is accurate, but much too computationally intensive for real problems. We propose to learn a spherical convolutional network that translates a planar CNN to process 360 imagery directly in its equirectangular projection. Our approach learns to reproduce the flat filter outputs on 360 data, sensitive to the varying distortion effects across the viewing sphere.