  Numerous prior studies predominantly emphasize constructing relation vectors
for individual neighborhood points and generating dynamic kernels for each
vector and embedding these into high-dimensional spaces to capture implicit
local structures. However, we contend that such implicit high-dimensional
structure modeling approch inadequately represents the local geometric
structure of point clouds due to the absence of explicit structural
information. Hence, we introduce X-3D, an explicit 3D structure modeling
approach. X-3D functions by capturing the explicit local structural information
within the input 3D space and employing it to produce dynamic kernels with
shared weights for all neighborhood points within the current local region.
This modeling approach introduces effective geometric prior and significantly
diminishes the disparity between the local structure of the embedding space and
the original input point cloud, thereby improving the extraction of local
features. Experiments show that our method can be used on a variety of methods
and achieves state-of-the-art performance on segmentation, classification,
detection tasks with lower extra computational cost, such as \textbf{90.7\%} on
ScanObjectNN for classification, \textbf{79.2\%} on S3DIS 6 fold and
\textbf{74.3\%} on S3DIS Area 5 for segmentation, \textbf{76.3\%} on ScanNetV2
for segmentation and \textbf{64.5\%} mAP , \textbf{46.9\%} mAP on SUN RGB-D and
\textbf{69.0\%} mAP , \textbf{51.1\%} mAP on ScanNetV2 . Our code is available
at
\href{https://github.com/sunshuofeng/X-3D}{https://github.com/sunshuofeng/X-3D}.
