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Found 12 papers, 10 papers with code
DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based Mapping
The experimental results demonstrate DeepAAT's substantial improvements over conventional AAT methods, highlighting its potential in the efficiency and accuracy of UAV-based 3D reconstruction tasks.
SparseDC: Depth Completion from sparse and non-uniform inputs
The key contributions of SparseDC are two-fold.
Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots
Our framework features a two-stream encoder, an active fusion decoder (AFD) and a dual-task regularization approach.
FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators
Matching cross-modality features between images and point clouds is a fundamental problem for image-to-point cloud registration.
CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration
Specifically, In the coarse matching phase, a novel I2P transformer module is employed to capture both homogeneous and heterogeneous global information from the image and point cloud data.
Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting
Previous multiview registration methods rely on exhaustive pairwise registration to construct a densely-connected pose graph and apply Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the scan poses.
CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloud
At present, the anchor-based or anchor-free models that use LiDAR point clouds for 3D object detection use the center assigner strategy to infer the 3D bounding boxes.
Consistent 3D Hand Reconstruction in Video via self-supervised Learning
We present a method for reconstructing accurate and consistent 3D hands from a monocular video.
KTNet: Knowledge Transfer for Unpaired 3D Shape Completion
The student network takes the incomplete one as input and restores the corresponding complete shape.
PC$^2$-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling
Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing a denser representation for the underlying surface.
AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding accurate normals in complex regions or containing noisy points.
Ranked #5 on
Surface Normals Estimation
on PCPNet
Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration
Then, we formulate the correspondence matching task as an energy function, which models the global similarity of keypoints on the hybrid spaces of BSC feature and Euclidean geometry.
