3D Gaussian Splatting (3DGS) has been explored for surface reconstruction, however unstructured and discontinuous Gaussian point clouds lead to uneven surface reconstruction accuracy as well as frequent loss of Novel View Synthesis (NVS) quality. To address this problem, we propose a scale-aligned monocular depth-guided 3DGS, a promising novel framework that combines geometric prior regularization and consistency supervision to achieve high-quality rendering and surface reconstruction. Specifically, monocular depth, estimated by some recently proposed monocular depth estimation models, contain implicitly abundant valuable geometric cues, but scale ambiguity limits its application. Therefore we first propose a $K$K-Nearest Neighbor (KNN)-based depth alignment framework that utilizes the full-domain gradient at monocular depth map to align to the sparse point cloud obtained during the Structure from Motion (SfM), which is employed for regularization to enhance geometric representation. Then a pseudo-mesh-based multi-view consistency module is introduced to fine-tune and guide the model to recover the accurate surface. Finally, a pixel-level isotropic gradient aware method guides the appropriate growth of the Gaussians to further improve the surface and rendering quality. Experiments on dozens of indoor, outdoor, and object-centered/non-object-centered datasets demonstrate that our method achieves accurate surface reconstruction with excellent NVS performance.

This paper presents a simultaneous localization and mapping (SLAM) system to provide accurate pose estimation and dynamic scene reconstruction. Our approach proposes a Joint Point-Gaussian Splatting representation, which fully integrates the robustness of isotropic feature points in pose estimation and the flexibility of anisotropic 3D Gaussians in scene representation. This system does not need to suppress the anisotropic representation of Gaussian elements, which enables the mapping module to achieve finer scene representation with lower memory consumption. Additionally, in order to enhance the adaptability of the system in dynamic environments, we introduced a dynamic region recognition module and utilized 3D Gaussian Splatting and 4D Gaussian Splatting representations to represent static and dynamic regions respectively. Furthermore, we developed a local map management strategy for Gaussian Splatting mapping, effectively reducing the memory and computational resource usage in the mapping process. Experiments on public datasets demonstrate that our system achieves state-of-the-art tracking and mapping accuracy compared to existing baselines.

The global optimization-based color correction approach aims to minimize the color differences of multiple images by optimizing the correction model for each image. The color differences in multisource and multitemporal remote sensing images are difficult to express using a simple correction model with few parameters. When employing a more flexible correction model, the number of correction parameters and optimization equations grows rapidly with the increase in the number and resolution of input images. In addition, the correction parameters of all images are coupled together and need to be solved simultaneously. An excessive number of parameters results in solving slowly or potential failure. To solve this problem, we propose a parallelizable color correction approach that decouples the correlation of correction parameters in the optimization equations and optimizes each image separately. First, we introduce auxiliary variables that replace values related to other images in the cost function. Second, we construct optimization equations for each image and parallelly solve the correction parameters. Finally, we correct the input images through a weighted correction model to better eliminate correction artifacts. Our approach iteratively optimizes auxiliary variables and correction parameters until the correction results converge. The experimental results on several challenging datasets show that our approach significantly improves execution efficiency and obtains the global optimal solution using the flexible correction model.

In the fields of photogrammetry and computer vision, three-dimensional (3D) urban building model reconstruction from airborne Light Detection and Ranging (LiDAR) point clouds has attracted significant attention in recent years. Accurately and automatically extracting local geometric structures, such as planar patches, from 3D point cloud data directly determines the quality of subsequent 3D model reconstruction. Considering that the roof is a crucial component of a real building, roof plane segmentation is a critical procedure in building 3D reconstruction. In this paper, a novel dual-branch transformer-based network is designed to accurately segment roof planes from airborne LiDAR point clouds. We first use PointNet++ followed with a transformer encoder to extract point-wise feature embeddings. Then, in the first branch, a transformer decoder module is applied to directly learn the instance centers of planar patches by giving a set of learned queries. Because the transformer can effectively model the relations of the queries and the global context information, the instance center positions of all planes included in the input point clouds can be accurately predicted. In this way, the number and center positions of roof planes are known before performing roof plane segmentation. In the second branch, we predict the offsets for each point using its point-wise feature to shift it towards the corresponding instance center. After that, the plane parameters for each plane instance can be estimated using the shifted points around the predicted centers, and the rest of points are assigned to its nearest plane to generate the final roof planes. The experimental results illustrate that our approach can successfully address the plane segmentation challenge for diverse building roof structures while achieving performance superior to the current state-of-the-art techniques. We will make the source code of our approach publicly available at https://github.com/Li-Li-Whu/PlaneTransformer.

We propose a novel, computationally efficient, and robust light detection and ranging (LiDAR)-only odometry framework based on truncated least squares termed T-LOAM. Our method focuses on alleviating the impact of outliers to allow robust navigation in sparse, noisy, or cluttered scenarios where degeneration occurs. As preprocessing, the multiregion ground extraction and dynamic curved-voxel clustering methods are proposed to accomplish the segmentation of 3D point clouds and filter out unstable objects. A novel feature extraction module is tailored to discriminate four peculiar features: edge features, sphere features, planar features, and ground features. As frontend, a hierarchical feature-based LiDAR-only odometry performs precise motion estimates through the truncated least squares method for directly processing various features. The preprocessing model and motion estimation precision have been evaluated on the KITTI odometry benchmark as well as various campus scenarios. The experimental results have demonstrated the real-time capability and superior precision of the proposed T-LOAM over other state-of-the-art algorithms.