Sparse 3D Reconstruction via Object-Centric Ray Sampling

We propose a novel method for 3D object reconstruction from a sparse set of views captured from a 360-degree calibrated camera rig. We represent the object surface through a hybrid model that uses both an MLP-based neural representation and a triangle mesh. A key contribution in our work is a novel object-centric sampling scheme of the neural representation, where rays are shared among all views. This efficiently concentrates and reduces the number of samples used to update the neural model at each iteration. This sampling scheme relies on the mesh representation to ensure also that samples are well-distributed along its normals. The rendering is then performed efficiently by a differentiable renderer. We demonstrate that this sampling scheme results in a more effective training of the neural representation, does not require the additional supervision of segmentation masks, yields state of the art 3D reconstructions, and works with sparse views on the Google's Scanned Objects, Tank and Temples and MVMC Car datasets

The Ninth Visual Object Tracking VOT2021 Challenge Results

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RDWT and SVD Based Secure Digital Image Watermarking Using ACM

It is demonstrated via computer simulations that the proposed RDWT, SVD and ACM based digital image watermarking scheme provides better watermark concealment and high robustness against both geometric and image processing attacks. Furthermore, the robustness of the proposed scheme is investigated for different dimensions of the binary watermark logo. It is shown that the robustness of our method is independent of the watermark size

Chaotic Digital Image Watermarking Scheme Based on DWT and SVD

In this study chaos based digital watermarking scheme together with Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) is proposed. In the proposed watermarking scheme, the cover image is decomposed to its sub-bands (LL, LH, HL and HH) by a common used frequency domain transform: DWT. Then, the SVD is directly applied to the all sub-bands of the decomposed cover image. The watermark is shuffled with Arnold's Cat Map (ACM) to generate a chaotic watermark. By this way, the robustness and perceptual invisibility of the scheme is improved. In order to evaluate the robustness of the proposed scheme, several image processing and geometric attacks are applied to the scheme. The Normalized correlation (NC) and peak signal-to-noise ratio (PSNR) measures are used to show the performance of the proposed method in terms of robustness and perceptual invisibility. The proposed algorithm gives the promising results and meets the security requirements