The rapid evolution of satellite imaging systems has resulted in sharp increases of image archive volumes. Multitemporal images constitute a sizeable portion of these time-series databases. Accordingly, development of accurate content based time-series retrieval (CBTSR) methods in massive archives of RS images attracts much research interest. Given a user-defined query time series, CBTSR aims at identifying within a massive archive image time series that show characteristics similar to those of the query time series. In this paper, we focus our attention to CBTSR in pairs of RS images, aiming to search and retrieve bi-temporal image pairs containing changes similar to those modeled in the query. To this end, we introduce two deep learning-based methods in the framework of CBTSR. The first method, called deep change vector retrieval (DVCR), is based on selected deep features extracted from the change vector analysis. The second method, called autoencoder with early fusion (AEEF) uses an autoencoder architecture to recreate the time difference images and the latent codes produced by this network. Experimental results show the effectiveness of the proposed methods for CBTSR problems. The code of the proposed methods is available at: https://github.com/OnatV/ChangeRetrieval.

Deep Learning Driven Content-Based Image Time-Series Retrieval in Remote Sensing Archives

The reconstruction of multi-layer 3D garments typically requires expensive multi-view capture setups and specialized 3D editing efforts. To support the creation of life-like clothed human avatars, we introduce ReMu for reconstructing multi-layer clothed humans in a new setup, Image Layers, which captures a subject wearing different layers of clothing with a single RGB camera. To reconstruct physically plausible multi-layer 3D garments, a unified 3D representation is necessary to model these garments in a layered manner. Thus, we first reconstruct and align each garment layer in a shared coordinate system defined by the canonical body pose. Afterwards, we introduce a collision-aware optimization process to address interpenetration and further refine the garment boundaries leveraging implicit neural fields. It is worth noting that our method is template-free and category-agnostic, which enables the reconstruction of 3D garments in diverse clothing styles. Through our experiments, we show that our method reconstructs nearly penetration-free 3D clothed humans and achieves competitive performance compared to category-specific methods. Project page: https://eth-ait.github.io/ReMu/

pdf/remu-reconstructing-multi-layer-3d-clothed-human-from-image-mlaso2a8n8nm.pdf

ReMu: Reconstructing Multi-layer 3D Clothed Human from Image Layers

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Deep Learning Driven Content-Based Image Time-Series Retrieval in Remote Sensing Archives

ReMu: Reconstructing Multi-layer 3D Clothed Human from Image Layers