其中,第3步需要用高维的searchsorted算子去寻找坐标值的索引,然而,目前MindSpore的searchsorted只支持1维输入,无法完成这一任务,暂时用pytorch的算子代替。 defsample_pdf(bins, weights, N_samples, det=False, pytest=False): weights = weights +1e-5pdf = weights / mnp.sum(weights, -1, keepdims=True) cd...
def sample_pdf(bins, weights, N_samples, det=False, pytest=False): weights = weights + 1e-5 pdf = weights / mnp.sum(weights, -1, keepdims=True) cdf = mnp.cumsum(pdf, -1) cdf = mnp.concatenate([mnp.zeros_like(cdf[..., :1]), cdf], -1) if det: u = mnp.linspace(0., ...
new_z_samples = sample_pdf(z_vals_mid, weights[..., 1:-1], n_samples, perturb=perturb) new_z_samples = new_z_samples.detach() # Resample points from ray based on PDF. z_vals_combined, _ = torch.sort(torch.cat([z_vals, new_z_samples], dim=-1), dim=-1) pts = rays_o[...
z_vals, _ = torch.sort(torch.cat([z_vals, z_samples], -1), -1) 下面主要介绍sample_pdf函数。 5.8 sample_pdf函数 在介绍该函数之前,需要说明一下,N_samples在参数中表示对粗网络采样的样本数,N_importance在参数中表示对细网络的采样数。但是sample_pdf函数的参数中, N_importance被写为了N_samples。
接下来就是基于概率密度的采样函数 sample_pdf 的分析了,这里以 weights[..., 1:-1] 切片传入,形状变为 [chunk, N_samples-1],不太懂为什么要排除第一个权重。 这里我们首先要参考论文中的 5.2 节 ,概率密度 pdf 的计算基于 PDF_i=w_i/\sum_{j=1}^{N_c}w_j ,概率分布 cdf 的计算基于 CDF_i...
samples = sample_pdf(bins, weights, N_samples, det=det) Volume Rendering During inference, first create_nerf is being called for loading the model weight and parameters and initiating the model. After that, the render function is called, which accepts the camera intrinsics, and the desired cam...
NeRF是一种生成模型,以图像和精确姿势为条件,生成给定图像的3D场景的新视图,这一过程通常被称为“新视图合成”。
def sample_pdf(bins, weights, N_samples, det=False, pytest=False): # Maxblur+weights_pad = torch.cat([weights[..., :1], weights, weights[..., -1:]], axis=-1)+weights_max = torch.maximum(weights_pad[..., :-1], weights_pad[..., 1:])+weights = 0.5 * (weights_max[.....
* near / rays_o[..., 2] rays_o = tf.stack([o0, o1, o2], -1) rays_d = tf.stack([d0, d1, d2], -1) return rays_o, rays_d # Hierarchical sampling helper def sample_pdf(bins, weights, N_samples, det=False): # Get pdf weights += 1e-5 # prevent nans pdf = ...
To render a 2D image, the following steps are taken: 1) sending camera rays through the scene to sample a set of points, 2) estimate density and color for each sampled point with Fψ and 3) exploit volume rendering [40] to synthesize the 2D image. In practice, the color C(r) ...