lvsm-pytorch 0.1.4

LVSM - Pytorch

LVSM - Pytorch

Implementation of LVSM, SOTA Large View Synthesis with Minimal 3d Inductive Bias, from Adobe Research

We will focus only on the Decoder-only architecture in this repository.

This paper lines up with another from ICLR 2025

Install

$ pip install lvsm-pytorch

Usage

import torch
from lvsm_pytorch import LVSM

rays = torch.randn(2, 4, 6, 256, 256)
images = torch.randn(2, 4, 3, 256, 256)

target_rays = torch.randn(2, 6, 256, 256)
target_images = torch.randn(2, 3, 256, 256)

model = LVSM(
    dim = 512,
    max_image_size = 256,
    patch_size = 32,
    depth = 2,
)

loss = model(
    input_images = images,
    input_rays = rays,
    target_rays = target_rays,
    target_images = target_images
)

loss.backward()

# after much training

pred_images = model(
    input_images = images,
    input_rays = rays,
    target_rays = target_rays,
) # (2, 3, 256, 256)

assert pred_images.shape == target_images.shape

Or from the raw camera intrinsic / extrinsics (please submit an issue or pull request if you see an error. new to view synthesis and out of my depths here)

import torch
from lvsm_pytorch import LVSM, CameraWrapper

input_intrinsic_rotation = torch.randn(2, 4, 3, 3)
input_extrinsic_rotation = torch.randn(2, 4, 3, 3)
input_translation = torch.randn(2, 4, 3)
input_uniform_points = torch.randn(2, 4, 3, 256, 256)

target_intrinsic_rotation = torch.randn(2, 3, 3)
target_extrinsic_rotation = torch.randn(2, 3, 3)
target_translation = torch.randn(2, 3)
target_uniform_points = torch.randn(2, 3, 256, 256)

images = torch.randn(2, 4, 4, 256, 256)
target_images = torch.randn(2, 4, 256, 256)

lvsm = LVSM(
    dim = 512,
    max_image_size = 256,
    patch_size = 32,
    channels = 4,
    depth = 2,
)

model = CameraWrapper(lvsm)

loss = model(
    input_intrinsic_rotation = input_intrinsic_rotation,
    input_extrinsic_rotation = input_extrinsic_rotation,
    input_translation = input_translation,
    input_uniform_points = input_uniform_points,
    target_intrinsic_rotation = target_intrinsic_rotation,
    target_extrinsic_rotation = target_extrinsic_rotation,
    target_translation = target_translation,
    target_uniform_points = target_uniform_points,
    input_images = images,
    target_images = target_images,
)

loss.backward()

# after much training

pred_target_images = model(
    input_intrinsic_rotation = input_intrinsic_rotation,
    input_extrinsic_rotation = input_extrinsic_rotation,
    input_translation = input_translation,
    input_uniform_points = input_uniform_points,
    target_intrinsic_rotation = target_intrinsic_rotation,
    target_extrinsic_rotation = target_extrinsic_rotation,
    target_translation = target_translation,
    target_uniform_points = target_uniform_points,
    input_images = images,
)

For an improvised self-supervised learning using masked autoencoder for reconstructing images and plucker rays, just import MAE first and wrap your LVSM instance. Then pass in your images and rays

import torch

from lvsm_pytorch import (
    LVSM,
    MAE
)

rays = torch.randn(2, 4, 6, 256, 256)
images = torch.randn(2, 4, 4, 256, 256)

lvsm = LVSM(
    dim = 512,
    max_image_size = 256,
    patch_size = 32,
    channels = 4,
    depth = 2,
    dropout_input_ray_prob = 0.5
)

mae = MAE(
    lvsm = lvsm,
    frac_masked = 0.5,                  # 1 in 2 image/ray pair to be masked out. minimum set to 1
    frac_images_to_ray_masked = 0.5,    # for a given image/ray pair that is masked, the proportion of images being masked vs rays (1. would be only images masked, 0. would be only rays masked). they cannot be both masked
    image_to_ray_loss_weight = 1.       # you can weigh the image recon oss differently than ray recon loss
)

ssl_loss = mae(
    images,
    rays
)

ssl_loss.backward()

# do the above in a loop on a huge amount of data

Above with camera in/extrsinsics

import torch

from lvsm_pytorch.lvsm import (
    LVSM,
    MAE,
    MAECameraWrapper
)

intrinsic_rotation = torch.randn(2, 4, 3, 3)
extrinsic_rotation = torch.randn(2, 4, 3, 3)
translation = torch.randn(2, 4, 3)
uniform_points = torch.randn(2, 4, 3, 256, 256)

images = torch.randn(2, 4, 4, 256, 256)

lvsm = LVSM(
    dim = 512,
    max_image_size = 256,
    patch_size = 32,
    channels = 4,
    depth = 2,
)

mae = MAE(lvsm)

model = MAECameraWrapper(mae)

loss = model(
    intrinsic_rotation = intrinsic_rotation,
    extrinsic_rotation = extrinsic_rotation,
    translation = translation,
    uniform_points = uniform_points,
    images = images,
)

loss.backward()

Citations

@inproceedings{Jin2024LVSMAL,
    title   = {LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias},
    author  = {Haian Jin and Hanwen Jiang and Hao Tan and Kai Zhang and Sai Bi and Tianyuan Zhang and Fujun Luan and Noah Snavely and Zexiang Xu},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:273507016}
}

@article{Zhang2024CamerasAR,
    title     = {Cameras as Rays: Pose Estimation via Ray Diffusion},
    author    = {Jason Y. Zhang and Amy Lin and Moneish Kumar and Tzu-Hsuan Yang and Deva Ramanan and Shubham Tulsiani},
    journal   = {ArXiv},
    year      = {2024},
    volume    = {abs/2402.14817},
    url       = {https://api.semanticscholar.org/CorpusID:267782978}
}

@misc{he2021masked,
    title   = {Masked Autoencoders Are Scalable Vision Learners}, 
    author  = {Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Dollár and Ross Girshick},
    year    = {2021},
    eprint  = {2111.06377},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}