Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface reconstruction framework via Gaussian Splatting. Our key idea is to leverage 2DGS as a bridge, which connects novel view synthesis to learned geometric priors and then transfers these priors to achieve surface reconstruction. Specifically, we incorporate a feed-forward network to predict per-view pixel-aligned 2DGS, which enables the network to synthesize novel view images and thus eliminates the need for direct 3D ground-truth supervision. To improve the accuracy of 2DGS position and orientation prediction, we propose a Weighted Chamfer Distance Loss to regularize the depth maps, especially in overlapping areas of input views, and also a normal prediction network to align the orientation of 2DGS with normal vectors predicted by a monocular normal estimator. Extensive experiments validate the effectiveness of our proposed improvement, demonstrating that our method achieves state-of-the-art performance in generalizable sparse-view mesh reconstruction tasks.

Depth estimation from indoor panoramas is challenging due to the equirectangular distortions of panoramas and inaccurate matching. In this paper, we propose a practical framework to improve the accuracy and efficiency of depth estimation from multi-view indoor panoramic images with the Neural Radiance Field technology. Specifically, we develop two networks to implicitly learn the Signed Distance Function for depth measurements and the radiance field from panoramas. We also introduce a novel spherical position embedding scheme to achieve high accuracy. For better convergence, we propose an initialization method for the network weights based on the Manhattan World Assumption. Furthermore, we devise a geometric consistency loss, leveraging the surface normal, to further refine the depth estimation. The experimental results demonstrate that our proposed method outperforms state-of-the-art works by a large margin in both quantitative and qualitative evaluations. Our source code is available at https://github.com/WJ-Chang-42/IndoorPanoDepth.

Leakage models, regarded as a bridge between the physical signal and the sensitive operation, have a great influence on the effectiveness of the side channel analysis. The existing leakage models are usually divided into two categories, the non-profiled leakage models which have been chosen before sampling and analyzing, such as Hamming weight and Hamming distance, while the profiled leakage models, whose parameters have to be trained in the profiling phase, such as the Stochastic model of which both coefficient vector and pooled covariance matrix are required to be estimated based on the acquired samples. In general, a profiled leakage model is more accurate than a non-profiled one. However, it may lead to an inefficient attack if the leakage function is inaccurate, e.g., the over-fitting and under-fitting in the profiling phase. In this paper, we mathematically prove the relationship among different stochastic models, and propose a new method named ECM to solve the problem that much time is required to solve matrix in the profiling phase. Replacing the observations in the matrix solution with the average signals, the new method accelerates the construction of any stochastic model significantly, as long as the data-dependent signal has the property equal images under different subkeys. On the basis of theoretical results, we analyze the reasons why over-fitting and under-fitting happen, and quantify the condition when some of them occur. Finally, comparing with the existing construction method (HSS2012), we verify the effectiveness and efficiency of ECM with different metrics. Under the same accuracy, the ECM obviously has lower time complexity than HSS2012.