X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition

TL;DR

X-3D introduces explicit 3D structure modeling to point cloud recognition, replacing implicit high-dimensional representations with explicit local structures extracted from the input space. By building a shared, region-specific structure kernel from the explicit structure and applying denoising plus restricted neighborhood context propagation, it tightens the coupling between embedding space and the original geometry. The approach yields state-of-the-art results across segmentation, classification, and detection with only modest computational overhead, and ablations/visualizations justify the benefits of explicit structure priors. Overall, X-3D provides a robust geometric prior that can be integrated into existing backbones to enhance local-feature extraction in non-Euclidean point clouds.

Abstract

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}.

Figures (5)

• Figure 1: Illustration of the different design paradigms. Implict High-dimensional Structure Modeling (IHSM). Most of existing work can be classified into this design paradigm, and the focus of modeling usually lies in how to construct relation vectors for each neighborhood point or how to generate a dynamic kernel for each vector and then embed the relation vectors to the high-dimension space to capture the implicit local structure. Explicit 3D Structure Modeling. The difference is that the basic unit of our modeling is the structure, and we directly build the explicit geometric structure for the local neighborhood in the input space, and generate the dynamic kernel which shares weights for all neighborhood points within the current local region through the geometric structure. By this explicit introduction of structural information into the embedding space, we greatly reduce the gap between the local structure captured by the embedding space and the original input point cloud.
• Figure 2: Illustration of X-3D. (a) first constructs the explicit local structure from the original input space, then reduces the influence of noise points on the local structure by cross attention, and finally generates the structure kernel by MLP. (b) avoids the influence caused by random neighborhood selection by propagating the neighborhood context,. Furthermore, by limiting the scope of dynamic context propagation, it ensures that the explicit local structure does not conflict
• Figure 3: Examples of local structures captured by different methods. (a) captures local structure by computing geodesic distances on the original input point cloud. (b) captures the implicit local structure through implicit high-dimensional structure modeling, and it can be seen that there is still a certain difference from the local structure of the original space represented by the geodesic distance. (c) captures the local structure through X-3D, and it can be seen that the local structure difference from the original space represented by the geodesic distance is small, and the representation in the edge part is more reasonable.
• Figure 4: Kmeans Results of the Structure Kernel. The parameters of structure kerne are restricted by local structure, which provides a good structural prior.
• Figure 5: We visualize the segmentation results of PointMetaBase-L as well as X-3D on S3DIS Area5 and annotate the different places