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import torch | ||
from torch import nn | ||
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class FeedForward(nn.Module): | ||
def __init__(self, dim, hidden_dim, dropout=0.): | ||
super().__init__() | ||
self.net = nn.Sequential( | ||
nn.Linear(dim, hidden_dim), nn.GELU(), nn.Dropout(dropout), | ||
nn.Linear(hidden_dim, dim), nn.Dropout(dropout)) | ||
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def forward(self, x): | ||
return self.net(x) | ||
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class PreNorm(nn.Module): | ||
def __init__(self, dim, fn): | ||
super().__init__() | ||
self.norm = nn.LayerNorm(dim) | ||
self.fn = fn | ||
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def forward(self, x, **kwargs): | ||
return self.fn(self.norm(x), **kwargs) | ||
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class FNetBlock(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
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def forward(self, x): | ||
x = torch.fft(torch.fft(x, signal_ndim=2), signal_ndim=1).real | ||
return x | ||
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class FNet(nn.Module): | ||
def __init__(self, dim, depth, mlp_dim, dropout=0.): | ||
super().__init__() | ||
self.layers = nn.ModuleList([]) | ||
for _ in range(depth): | ||
self.layers.append( | ||
nn.ModuleList([ | ||
PreNorm(dim, FNetBlock()), | ||
PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout)) | ||
])) | ||
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def forward(self, x): | ||
for attn, ff in self.layers: | ||
x = attn(x) + x | ||
x = ff(x) + x | ||
return x |
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# Learning Local Displacements for Point Cloud Completion | ||
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BSD 3-Clause License Copyright (c) 2022, Yida Wang All rights reserved. | ||
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## Abstrarct | ||
| Completing a car | | | ||
| :-: | :-- | | ||
![teaser](readme_imgs/CVPR_teaser.png#center) | From the input partial scan to our object completion, we visualize the amount of detail in our reconstruction. | ||
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We propose a novel approach aimed at object and semantic scene completion from a partial scan represented as a 3D point cloud. | ||
Our architecture relies on three novel layers that are used successively within an encoder-decoder structure and specifically developed for the task at hand. | ||
The first one carries out feature extraction by matching the point features to a set of pre-trained local descriptors. | ||
Then, to avoid losing individual descriptors as part of standard operations such as max-pooling, we propose an alternative neighbor-pooling operation that relies on adopting the feature vectors with the highest activations. Finally, up-sampling in the decoder modifies our feature extraction in order to increase the output dimension. | ||
While this model is already able to achieve competitive results with the state of the art, we further propose a way to increase the versatility of our approach to process point clouds. To this aim, we introduce a second model that assembles our layers within a transformer architecture. | ||
We evaluate both architectures on object and indoor scene completion tasks, achieving state-of-the-art performance. | ||
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## Methodology | ||
### Local displacement operator | ||
| The operation | | | ||
| :-: | :-- | | ||
![operator](readme_imgs/CVPR_graph_conv.png#center) | (a) *k*-nearest neighbor in reference to an anchor **f**; (b) displacement vectors around the anchor **f** + δ<sub>i</sub> and the corresponding weight σ<sub>i</sub>; and, (c) closest features for all i. | ||
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### Architectures | ||
| The *direct* architectrue | The *transformer* architecture | | ||
| :-: | :-: | | ||
![direct](readme_imgs/CVPR_direct_architecture.png#center) | ![transformer](readme_imgs/CVPR_transformer_architecture.png#center) | ||
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### Qualitatives | ||
#### Object completion | ||
![objects](readme_imgs/CVPR_shapenet.png#center) | ||
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#### Semantic scene completion | ||
![objects](readme_imgs/CVPR_scannet.png#center) | ||
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## Cite | ||
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If you find this work useful in your research, please cite: | ||
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```bash | ||
@inproceedings{wang2022displacement, | ||
title={Learning Local Displacements for Point Cloud Completion}, | ||
author={Wang, Yida and Tan, David Joseph and Navab, Nassir and Tombari, Federico}, | ||
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, | ||
year={2022} | ||
} | ||
``` |
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import torch | ||
import torch.nn as nn | ||
import torch.nn.parallel | ||
import torch.utils.data | ||
from torch.autograd import Variable | ||
import numpy as np | ||
import torch.nn.functional as F | ||
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class Periodics(nn.Module): | ||
def __init__(self, | ||
dim_input=2, | ||
dim_output=512, | ||
is_first=True, | ||
transpose=False): | ||
super(Periodics, self).__init__() | ||
self.dim_input = dim_input | ||
self.dim_output = dim_output | ||
self.is_first = is_first | ||
self.transpose = transpose | ||
self.with_frequency = True | ||
self.with_phase = True | ||
# Omega determines the upper frequencies | ||
self.omega_0 = 30 | ||
if self.with_frequency: | ||
if self.with_phase: | ||
self.Li = nn.Conv1d( | ||
self.dim_input, self.dim_output, 1, | ||
bias=self.with_phase).cuda() | ||
else: | ||
self.Li = nn.Conv1d( | ||
self.dim_input, | ||
self.dim_output // 2, | ||
1, | ||
bias=self.with_phase).cuda() | ||
# nn.init.normal_(B.weight, std=10.0) | ||
with torch.no_grad(): | ||
if self.is_first: | ||
self.Li.weight.uniform_(-1 / self.dim_input, | ||
1 / self.dim_input) | ||
else: | ||
self.Li.weight.uniform_( | ||
-np.sqrt(6 / self.dim_input) / self.omega_0, | ||
np.sqrt(6 / self.dim_input) / self.omega_0) | ||
else: | ||
self.Li = nn.Conv1d(self.dim_input, self.dim_output, 1).cuda() | ||
self.BN = nn.BatchNorm1d(self.dim_output).cuda() | ||
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def filter(self): | ||
filters = torch.cat([ | ||
torch.ones(1, self.dim_output // 32 * 32), | ||
torch.zeros(1, self.dim_output // 32 * 0) | ||
], 1).cuda() | ||
filters = torch.unsqueeze(filters, 2) | ||
return filters | ||
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def forward(self, x): | ||
if not torch.is_tensor(x): |
Oops, something went wrong.