3d Vision
Showing 14 of 14 projects
A differentiable computer vision library for PyTorch, providing geometric vision and image processing algorithms for AI workflows.
A curated list of papers, datasets, and code for 3D point cloud analysis research, covering classification, segmentation, detection, and more.
A large-scale dataset of object-centric video clips with 3D bounding box annotations and AR metadata for 3D object detection research.
An efficient neural network for semantic segmentation of large-scale 3D point clouds using random sampling.
A deep learning framework for feature learning directly from point clouds using X-Conv operations, achieving state-of-the-art results in classification and segmentation.
A PyTorch implementation of self-supervised monocular depth estimation using 3D packing for high-resolution, real-time depth prediction.
A CUDA-accelerated library collection for point cloud processing, providing GPU-optimized alternatives to PCL functions.
A fast and robust ground segmentation algorithm for 3D LiDAR point clouds, using concentric zone-based region-wise processing.
A ROS-based object detection and pose estimation library for 2D and 3D applications using OpenCV.
A PyTorch framework for deep learning on point clouds, providing a modular and reproducible foundation for 3D vision tasks.
A self-supervised deep learning model for extrinsic calibration between LiDAR and camera sensors using 3D spatial transformer networks.
A deep learning approach that unifies global place recognition and local 6DoF pose refinement for robust relocalization in large-scale 3D point clouds.
Library and utilities for working with ifm pmd-based 3D Time-of-Flight cameras, supporting O3R, O3D, and O3X platforms.
A graphical user interface for annotating point clouds and 3D scenes with bounding boxes, keypoints, and rectangles.
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