Interpolation module.

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

MLP as used in Vision Transformer, MLP-Mixer and related networks

Residual convolution module.

Adapt croco's DPTOutputAdapter implementation for dust3r: remove duplicated weigths, and fix forward for dust3r

Adapt croco's DPTOutputAdapter implementation for dust3r: remove duplicated weigths, and fix forward for dust3r

Two siamese encoders, followed by two decoders. The goal is to output 3d points directly, both images in view1's frame (hence the asymmetry).

Feature fusion block.

Linear head for dust3r Each token outputs: - 16x16 3D points (+ confidence)

just adding _init_weights + position getter compared to timm.models.layers.patch_embed.PatchEmbed

DPT module for dust3r, can return 3D points + confidence for all pixels

DPT module for dust3r, can return 3D points + confidence for all pixels

return positions of patches

random masking

Ensure that all 3D points are correct. Asymmetric loss: view1 is supposed to be the anchor. P1 = RT1 @ D1 P2 = RT2 @ D2

Wraps a dataset so that PyTorch Lightning's validation step can be turned into a visualization step.

Two siamese encoders, followed by two decoders. The goal is to output 3d points directly, both images in view1's frame (hence the asymmetry).

Two siamese encoders, followed by two decoders. The goal is to output 3d points directly, both images in view1's frame (hence the asymmetry).

Weighted regression by learned confidence. Assuming the input pixel_loss is a pixel-level regression loss. Principle: high-confi

DPT output adapter. :param num_cahnnels: Number of output channels :param stride_level: tride level compared to the full-sized image.

Euclidean distance between 3d points

L-norm loss

Linear head for GS parameter prediction Each token outputs: - 16x16 3D points (+ confidence)

Handle images with non-square aspect ratio. All images in the same batch have the same aspect ratio. true_shape = [(height, width) ..