This article describes three algorithms for distance field generation on triangulated model: brute force algorithm, single-threaded algorithm based on spatial partition and multi-threaded algorithm based on spatial pa...This article describes three algorithms for distance field generation on triangulated model: brute force algorithm, single-threaded algorithm based on spatial partition and multi-threaded algorithm based on spatial partition. Spatial partition algorithm use equidistant network divide the bounding box into equal-sized cubes, calculates the maximum and minimum distances between the sample point and each of the small cubes,taking the minimum value from the maximum distance as the minimum distance from the sample point to the model named d1, comparing d1 with the distance from sample point to every little cube's minimum distance d2, if d1 <d2, the sample point's distance to all triangles inside this cube are greater than d1, skip this cube, otherwise, calculated the distance from the point to all the triangles intersect with the cube, then alternative d1 with the minimum value, circulate all small cubes intersect with the model. Comparing the calculation results, it can be seen that the algorithm about the multi-threaded distance field relative to the other two algorithms in computational speed is greatly improved especially for complex models.展开更多
将神经网络用于场景几何材质的高效表达,结合逆向渲染在二维光度图的监督下重建高质量的网格和材质贴图,为现有的图形学流水线提供服务——神经渲染已成为近年来计算机图形学新的研究热点。在IRON(inverse rendering by optimizing neur...将神经网络用于场景几何材质的高效表达,结合逆向渲染在二维光度图的监督下重建高质量的网格和材质贴图,为现有的图形学流水线提供服务——神经渲染已成为近年来计算机图形学新的研究热点。在IRON(inverse rendering by optimizing neural SDFs and materials from photometric images)神经渲染模型基础上,通过引入多分辨率哈希编码,采用冻结训练等方法提高原始模型的训练速度。在多个数据集上的对比实验表明,优化后的IRON逆渲染模型训练速度提升了约40%,且重建结果中包含更多细节。展开更多
文摘This article describes three algorithms for distance field generation on triangulated model: brute force algorithm, single-threaded algorithm based on spatial partition and multi-threaded algorithm based on spatial partition. Spatial partition algorithm use equidistant network divide the bounding box into equal-sized cubes, calculates the maximum and minimum distances between the sample point and each of the small cubes,taking the minimum value from the maximum distance as the minimum distance from the sample point to the model named d1, comparing d1 with the distance from sample point to every little cube's minimum distance d2, if d1 <d2, the sample point's distance to all triangles inside this cube are greater than d1, skip this cube, otherwise, calculated the distance from the point to all the triangles intersect with the cube, then alternative d1 with the minimum value, circulate all small cubes intersect with the model. Comparing the calculation results, it can be seen that the algorithm about the multi-threaded distance field relative to the other two algorithms in computational speed is greatly improved especially for complex models.
文摘将神经网络用于场景几何材质的高效表达,结合逆向渲染在二维光度图的监督下重建高质量的网格和材质贴图,为现有的图形学流水线提供服务——神经渲染已成为近年来计算机图形学新的研究热点。在IRON(inverse rendering by optimizing neural SDFs and materials from photometric images)神经渲染模型基础上,通过引入多分辨率哈希编码,采用冻结训练等方法提高原始模型的训练速度。在多个数据集上的对比实验表明,优化后的IRON逆渲染模型训练速度提升了约40%,且重建结果中包含更多细节。
