Differentiable Convolution Search for Point Cloud Processing
- URL: http://arxiv.org/abs/2108.12856v1
- Date: Sun, 29 Aug 2021 14:42:03 GMT
- Title: Differentiable Convolution Search for Point Cloud Processing
- Authors: Xing Nie, Yongcheng Liu, Shaohong Chen, Jianlong Chang, Chunlei Huo,
Gaofeng Meng, Qi Tian, Weiming Hu, Chunhong Pan
- Abstract summary: We propose a novel differential convolution search paradigm on point clouds.
It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling.
We also propose a joint optimization framework for simultaneous search of internal convolution and external architecture, and introduce epsilon-greedy algorithm to alleviate the effect of discretization error.
- Score: 114.66038862207118
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Exploiting convolutional neural networks for point cloud processing is quite
challenging, due to the inherent irregular distribution and discrete shape
representation of point clouds. To address these problems, many handcrafted
convolution variants have sprung up in recent years. Though with elaborate
design, these variants could be far from optimal in sufficiently capturing
diverse shapes formed by discrete points. In this paper, we propose
PointSeaConv, i.e., a novel differential convolution search paradigm on point
clouds. It can work in a purely data-driven manner and thus is capable of
auto-creating a group of suitable convolutions for geometric shape modeling. We
also propose a joint optimization framework for simultaneous search of internal
convolution and external architecture, and introduce epsilon-greedy algorithm
to alleviate the effect of discretization error. As a result, PointSeaNet, a
deep network that is sufficient to capture geometric shapes at both convolution
level and architecture level, can be searched out for point cloud processing.
Extensive experiments strongly evidence that our proposed PointSeaNet surpasses
current handcrafted deep models on challenging benchmarks across multiple tasks
with remarkable margins.
Related papers
- StarNet: Style-Aware 3D Point Cloud Generation [82.30389817015877]
StarNet is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network.
Our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks.
arXiv Detail & Related papers (2023-03-28T08:21:44Z) - Parametric Surface Constrained Upsampler Network for Point Cloud [33.033469444588086]
We introduce a novel surface regularizer into the upsampler network by forcing the neural network to learn the underlying parametric surface represented by bicubic functions and rotation functions.
These designs are integrated into two different networks for two tasks that take advantages of upsampling layers.
The state-of-the-art experimental results on both tasks demonstrate the effectiveness of the proposed method.
arXiv Detail & Related papers (2023-03-14T21:12:54Z) - Shrinking unit: a Graph Convolution-Based Unit for CNN-like 3D Point
Cloud Feature Extractors [0.0]
We argue that a lack of inspiration from the image domain might be the primary cause of such a gap.
We propose a graph convolution-based unit, dubbed Shrinking unit, that can be stacked vertically and horizontally for the design of CNN-like 3D point cloud feature extractors.
arXiv Detail & Related papers (2022-09-26T15:28:31Z) - Autoregressive 3D Shape Generation via Canonical Mapping [92.91282602339398]
transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and text generation.
In this paper, we aim to further exploit the power of transformers and employ them for the task of 3D point cloud generation.
Our model can be easily extended to multi-modal shape completion as an application for conditional shape generation.
arXiv Detail & Related papers (2022-04-05T03:12:29Z) - Dynamic Convolution for 3D Point Cloud Instance Segmentation [146.7971476424351]
We propose an approach to instance segmentation from 3D point clouds based on dynamic convolution.
We gather homogeneous points that have identical semantic categories and close votes for the geometric centroids.
The proposed approach is proposal-free, and instead exploits a convolution process that adapts to the spatial and semantic characteristics of each instance.
arXiv Detail & Related papers (2021-07-18T09:05:16Z) - Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
We propose a novel end-to-end learning-based framework to generate dense point clouds.
We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors.
Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
arXiv Detail & Related papers (2020-11-25T14:00:18Z) - DV-ConvNet: Fully Convolutional Deep Learning on Point Clouds with
Dynamic Voxelization and 3D Group Convolution [0.7340017786387767]
3D point cloud interpretation is a challenging task due to the randomness and sparsity of the component points.
In this work, we draw attention back to the standard 3D convolutions towards an efficient 3D point cloud interpretation.
Our network is able to run and converge at a considerably fast speed, while yields on-par or even better performance compared with the state-of-the-art methods on several benchmark datasets.
arXiv Detail & Related papers (2020-09-07T07:45:05Z) - Progressive Point Cloud Deconvolution Generation Network [37.50448637246364]
We propose an effective point cloud generation method, which can generate multi-resolution point clouds of the same shape from a latent vector.
By concatenating different resolutions of local and global feature maps, we employ the multi-layer perceptron as the generation network to generate multi-resolution point clouds.
In order to keep the shapes of different resolutions of point clouds consistent, we propose a shape-preserving adversarial loss to train the point cloud deconvolution generation network.
arXiv Detail & Related papers (2020-07-10T13:07:00Z) - Permutation Matters: Anisotropic Convolutional Layer for Learning on
Point Clouds [145.79324955896845]
We propose a permutable anisotropic convolutional operation (PAI-Conv) that calculates soft-permutation matrices for each point.
Experiments on point clouds demonstrate that PAI-Conv produces competitive results in classification and semantic segmentation tasks.
arXiv Detail & Related papers (2020-05-27T02:42:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.