Recently, color point cloud registration has begun to receive attention. Unlike geometric-only point clouds, color point clouds incorporate additional color information. Therefore, color point cloud registration can achieve higher accuracy than geometric-only point cloud registration. Despite the success, existing methods are computationally intensive due to the high resource demands of Transformer. In this paper, we propose a Mamba architecture based registration algorithm, CGFMamba-PCR, with color-geometric fusion. Specifically, we propose CGFMamba, a novel color-geometric fusion Mamba network for color point cloud registration, which enhances feature representation through color-geometric guided point ordering and positional encoding. For the input color point cloud pair, they are passed through CGFMamba based feature extraction module to obtain their corresponding features. Then, these features are passed through feature matching and outlier rejection modules to obtain final registration result. Furthermore, an ordering method for the Mamba architecture is proposed that clusters color hue and sorts spatial coordinates. Experiments on Color3DMatch and Color3DLoMatch datasets demonstrate that the proposed algorithm outperforms the state-of-the-art (SOTA) methods. The code of the proposed algorithm will be open-sourced upon acceptance of this paper.

We propose 3D-SLNR, a new and ultra-lightweight neural representation with outstanding performance for large-scale 3D mapping. The representation defines a global signed distance function (SDF) in near-surface space based on a set of band-limited local SDFs anchored at support points sampled from point clouds. These SDFs are parameterized only by a tiny multi-layer perceptron (MLP) with no latent features, and the state of each SDF is modulated by three learnable geometric properties: position, rotation, and scaling, which make the representation adapt to complex geometries. Then, we develop a novel parallel algorithm tailored for this unordered representation to efficiently detect local SDFs where each sampled point is located, allowing for real-time updates of local SDF states during training. Additionally, a prune-and-expand strategy is introduced to enhance adaptability further. The synergy of our low-parameter model and its adaptive capabilities results in an extremely compact representation with excellent expressiveness. Extensive experiments demonstrate that our method achieves state-of-the-art reconstruction performance with less than 1/5 of the memory footprint compared with previous advanced methods.

This paper proposes a fully differentiable and end-to-end framework for learning Bézier decomposition on 3D point clouds. The framework aims to partition input point clouds into multiple Bézier primitive patches through a learned Bézier decomposition process. Unlike previous approaches that handle different primitive types separately, thus being limited to specific shape categories, our method seeks to achieve a generalized primitive segmentation on point clouds. Drawing inspiration from Bézier decomposition on NURBS models, we adapt it to guide point cloud segmentation without relying on pre-defined primitive types. To achieve this, we introduce a joint optimization framework that simultaneously learns Bézier primitive segmentation and geometric fitting in a cascaded architecture. Additionally, we propose a soft voting regularizer to enhance primitive segmentation and an auto-weight embedding module to effectively cluster point features, making the network more robust and applicable to various scenarios. Furthermore, we incorporate a reconstruction module capable of processing multiple CAD models with different primitives simultaneously. Extensive experiments were conducted on both synthetic ABC datasets and real-scan datasets to validate and compare our approach against several baseline methods. The results demonstrate that our method outperforms previous work in terms of segmentation accuracy, while also exhibiting significantly faster inference speed.

This paper addresses the challenge of accurately estimating unoriented normals for 3D point clouds, particularly in the presence of noise, varying densities, and sharp features. To overcome these challenges, we introduce a novel network architecture that integrates a geometry-weighted transformer module with a dual backbone network. This combined architecture enables accurate prediction of normals from patch embeddings that encode edge and spatial features. Additionally, we propose a region-growing loss to supervise the network, which can aid in normal estimation for sharp features by segmenting input patches based on normal discontinuities, thus avoiding the over-smoothness for normals in the sharp feature areas. Extensive experiments on PCPNet and SceneNN datasets highlight the effectiveness of our approach, leading to state-of-the-art performance against baseline methods.