Extending the success of 2D Large Kernel to 3D perception is challenging due
to: 1. the cubically-increasing overhead in processing 3D data; 2. the
optimization difficulties from data scarcity and sparsity. Previous work has
taken the first step to scale up the kernel size from 3x3x3 to 7x7x7 by
introducing block-shared weights. However, to reduce the feature variations
within a block, it only employs modest block size and fails to achieve larger
kernels like the 21x21x21. To address this issue, we propose a new method,
called LinK, to achieve a wider-range perception receptive field in a
convolution-like manner with two core designs. The first is to replace the
static kernel matrix with a linear kernel generator, which adaptively provides
weights only for non-empty voxels. The second is to reuse the pre-computed
aggregation results in the overlapped blocks to reduce computation complexity.
The proposed method successfully enables each voxel to perceive context within
a range of 21x21x21. Extensive experiments on two basic perception tasks, 3D
object detection and 3D semantic segmentation, demonstrate the effectiveness of
our method. Notably, we rank 1st on the public leaderboard of the 3D detection
benchmark of nuScenes (LiDAR track), by simply incorporating a LinK-based
backbone into the basic detector, CenterPoint. We also boost the strong
segmentation baseline's mIoU with 2.7% in the SemanticKITTI test set. Code is
available at this https URL.

提出了一种名为 LinK 的方法，通过使用线性卷积核生成器和重复利用重叠块中预先计算的聚合结果来扩展感受野，从而成功实现每个体素在 21x21x21 范围内感知上下文，并在三维物体检测和语义分割任务中取得了显着表现。

LinK: 基于激光雷达的三维感知的线性核

LinK: Linear Kernel for LiDAR-based 3D Perception

Voxel is an important format to represent geometric data, which has been
widely used for 3D deep learning in shape analysis due to its generalization
ability and regular data format. However, fine-grained tasks like part
segmentation require detailed structural information, which increases voxel
resolution and thus causes other issues such as the exhaustion of computational
resources. In this paper, we propose a novel volumetric convolutional neural
network, which could extract discriminative features encoding detailed
information from voxelized 3D data under a limited resolution. To this purpose,
a spatial dense extraction (SDE) module is designed to preserve the spatial
resolution during the feature extraction procedure, alleviating the loss of
detail caused by sub-sampling operations such as max-pooling. An attention
feature aggregation (AFA) module is also introduced to adaptively select
informative features from different abstraction scales, leading to segmentation
with both semantic consistency and high accuracy of details. Experiment results
on the large-scale dataset demonstrate the effectiveness of our method in 3D
shape part segmentation.

本文提出了一种新颖的体积卷积神经网络，通过空间密集提取模块和注意力特征聚合模块从体素化三维数据中提取具有详细信息的判别特征，以实现具有语义一致性和高精度细节的分割任务，实验结果表明了在大规模数据集上进行形状部分分割任务时我们方法的有效性。