Modification based on TORCHVISION.UTILS ::param: tensor (batch, channel, H, W)

Normalize a vector.

supplementary fix_coord() for direct matching Inputs: args: parsed argument pose: pose [N, 3, 4] device: cpu or gpu Ou

Normalize a vector.

Saves model when validation loss decrease.

Inference the Pose Regression Network Inputs: args: parsed argument data: Input image in shape (batchsize, channels, H, W)

:param mapnet: the MapNet (two CNN blocks inside the green block in Fig. 2 of paper). Not to be confused with MapNet, the model!

Instantiate NeRF's MLP model.

set whole model to requires_grad=False, this is for nerf model

set whole model to requires_grad=False, this is for nerf model

fix coord for 7 Scenes to align with llff style dataset

fix coord for 7 Scenes to align with llff style dataset

set batchnorm to eval() it is useful to align train and testing result

Inference the Pose Regression Network Inputs: args: parsed argument data: Input image in shape (batchsize, channels, H, W)

Load a pretrained DFNet model

Initialize one adaptation layer for every extraction point. Args: hypercolumn_layers: The list of the hypercolumn layer names.

Center the poses so that we can use NDC. See https://github.com/bmild/nerf/issues/34 Inputs: poses: (N_images, 3, 4) pos

Center the poses so that we can use NDC. See https://github.com/bmild/nerf/issues/34 Inputs: poses: (N_images, 3, 4) pos

freeze bn layer by not require grad but still behave differently when model.train() vs. model.eval()

Convert Rotation matrix to quaternion, then calculate the location errors. original from PoseNet Paper

Convert Rotation matrix to quaternion, then calculate the location errors. original from PoseNet Paper

Data loader for Pose Regression Network

Data loader for Pose Regression Network

Constructs a version of 'fn' that applies to smaller batches.

initialize camera frustum parameters

Compute Feature MSE Loss :param: feature_rgb, [C,H,W] or [C, N_rand] :param: feature_target, [C,H,W] or [C, N_rand] :param: img_in, True

print feature maps :param features: (3, [batch, H, W]) or [3, batch, H, W] :param path: save image path :param isList: wether the fea

Break batch of images into rays

transform output features from the network to required shape for computing loss :param: feature [L, B, C, H, W] # L stands for level of featu

Convert NeRFW fine network output to rendered colors This version is implemented in nerf_pl https://github.com/kwea123/nerf_pl/tree/nerfw Inp

render nerfw imgs, save unscaled pose and results

Compute RGB MSE Loss, original from NeRF Paper

r""" From Kornia. Convert an RGB image to YUV. .. image:: _static/img/rgb_to_yuv.png The image data is assumed to be in the range of

Prepares inputs and applies network 'fn'.

Modification based on TORCHVISION.UTILS, save single feature map ::param: tensor (batch, channel, H, W)

finetune pretrained PoseNet using NeRF

triplet loss with hard negative mining, four cases. inspired by http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf section3.3 :para

processes the 1x12 raw pose from dataset by aligning and then normalizing :param poses_in: N x 12 :param mean_t: 3 :param std_t: 3 :return:

assume target and nerf rgb are inferenced at the same time, slice target batch and nerf batch and output stacked features :pa

assume target and nerf rgb are inferenced at the same time, slice target batch and nerf batch and output stacked features :pa

Calculate the average pose, which is then used to center all poses using @center_poses. Its computation is as follows: 1. Compute the cen

Calculate the average pose, which is then used to center all poses using @center_poses. Its computation is as follows: 1. Compute the cen

Render rays in smaller minibatches to avoid OOM.

Instantiate NeRF's MLP model.

Nerfie paper section 3.5 Coarse-to-Fine Deformation Regularization

Nerfie paper section 3.5 Coarse-to-Fine Deformation Regularization

Perform 1 step of eval

Perform 1 step of eval

Perform 1 epoch of training with batch

Perform 1 epoch of training with batch

Nerfie Paper Eq.(8)

Nerfie Paper Eq.(8)

Inverse warp images in batch :param img: batch of images tensor [batch_size, 1, H, W] :param mat_homo_inv: batch

Data loader for NeRF

Data loader for NeRF

test model

visualizes the dataset

visualizes the dataset

Inputs: poses: (3, 4) x: translational perturb range angle: rotation angle perturb range in degrees Outputs:

Break batch of images into rays

prepare data for ready to train posenet, return dataloaders

print feature maps ::param: features

processes the 1x12 raw pose from dataset by aligning and then normalizing produce logq :param poses_in: N x 12 :param mean_t: 3 :param std_