
In the following slides, distance refers to Euclidean distance Brixelizer is a library that generates sparse distance fields in real-time for any given triangle-based
[2206.05737] SparseNeuS: Fast Generalizable Neural Surface ...
2022年6月12日 · SparseNeuS adopts signed distance function (SDF) as the surface representation, and learns generalizable priors from image features by introducing geometry encoding volumes for generic surface prediction.
好烦的SDF 与Mesh/体积网格碰撞处理 - 知乎
2023年5月29日 · 在Graphics物理仿真领域,SDF(有向距离场)作为一种优秀的静态边界条件表示方法,以其简单的表示方式(grid distance)、高效的距离查询(primitive单独查询距离场,三线性插值便可提供可观的精度)、较为独立的碰撞约束(particle based只需要沿着法向插值平移到 ...
GitHub - cvlab-stonybrook/s-volsdf: Official implementation of "S ...
This is the official implementation of S-VolSDF: Sparse Multi-View Stereo Regularization of Neural Implicit Surfaces. Haoyu Wu, Alexandros Graikos, Dimitris Samaras. International Conference on Computer Vision (ICCV), 2023. The code is compatible with …
SDF (signed distance field)基础理论和计算 - 知乎
SDF (Signed Distance Field)在3d和2d中都有对应的应用。 在3d中光线追踪对于性能的消耗过大,所以sdf常常被用来作为物体的隐式表达,配合 ray marching 达到接近光线追踪的效果,也有比如 deepSDF 这种对于模型的隐式表达方面的应用。
SparseNeuS: 从稀疏视图快速泛化神经表面重建 - 知乎
2022年12月6日 · SparseNeuS 采用 符号距离函数 (SDF)作为表面表示,并通过引入用于通用表面预测的几何编码体积来从 图像特征 中学习可概括的先验。 此外,还引入了几种有效利用稀疏视图进行高质量重建的策略,包括:1)多级几何推理框架,以从粗到细的方式恢复表面;2) 多尺度颜色混合方案,用于更可靠的颜色预测;3)一致性感知微调方案来控制由遮挡和噪声引起的不一致区域。 广泛的实验表明,我们的方法不仅优于最先进的方法,而且表现出良好的效率, 管 …
Signed Distance Field (SDF) - GitHub
2009年10月12日 · Fluid and Particle Physics in PixelJunk Shooter. 2D fluids collision detection with SDF https://www.gdcvault.com/play/1012447/Go-With-the-Flow-Fluid Realtime sparse …
One-2-3-45/reconstruction/models/sparse_sdf_network.py at …
[NeurIPS 2023] Official code of "One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization" - One-2-3-45/reconstruction/models/sparse_sdf_network.py at …
S-VolSDF - GitHub Pages
Given only three sparse input views, experiments show that our method not only outperforms generic neural rendering models by a large margin but also significantly increases the reconstruction quality of MVS models.
SparseNeuS adopts signed distance function (SDF) as the surface representation, and learns generalizable priors from image features by introducing geometry encoding volumes for generic surface prediction.