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PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without voxelization. It provides a system for 3D object classification, semantic segmentation frameworks for partitioning clouds into categories, and tools for visualizing 3D shapes. The project utilizes a transform network to align point clouds into a canonical coordinate space and employs symmetric-function-based aggregation to condense point-wise features into global vectors regardless of point order. It also features a multi-scale grouping architecture to extract hierarchical geometric
SpatialLM is a spatial modeling framework that uses large language models to transform monocular video and sensor data into structured indoor semantic maps. It functions as a system for indoor layout estimation and a point cloud semantic parser, converting raw geometric data into representations of architectural elements and object categories. The project aligns multi-modal sensor inputs with linguistic tokens, allowing a language model to serve as a reasoning engine for inferring room topology. It employs mechanisms to convert 3D point clouds and 2D image sequences into discrete tokens and s
CloudCompare is a professional software application for processing and analyzing 3D point clouds and polygonal meshes. It functions as a 3D mesh analysis tool and a large dataset visualizer designed to display and manage millions of points in a 3D environment. The software provides specialized capabilities for point cloud comparison, utilizing an optimized octree structure to calculate spatial differences between two 3D datasets. This allows for the identification of variations and errors between point clouds or between a point cloud and a mesh. The system covers broad 3D data analysis areas
MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate
Paper
The main features of open-air-sun/pq-transformer are: Point Cloud and 3D Processing, Scene Understanding.
Projects with overlapping indexed features include: manycore-research/spatiallm — SpatialLM is a spatial modeling framework that uses large language models to transform monocular video and sensor data… open-mmlab/openpcdet — OpenPCDet is a PyTorch deep learning library and toolbox for LiDAR 3D object detection. It functions as a point cloud… cloudcompare/cloudcompare — CloudCompare is a professional software application for processing and analyzing 3D point clouds and polygonal meshes.… charlesq34/pointnet — PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without… open-mmlab/mmdetection3d — MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting… chenfengxu714/yogo — Official implementation of YOGO for point-cloud processing.