Projects sharing features with SpatialDETR

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

OpenPCDet is a PyTorch deep learning library and toolbox for LiDAR 3D object detection. It functions as a point cloud processing framework designed to develop, train, and evaluate machine learning models that identify and locate objects in three dimensional space. The project includes a GPU-accelerated geometry engine for high-performance implementation of 3D intersection over union and rotated non-maximum suppression. It also provides a distributed model training tool to scale the training and testing of detection models across multiple GPUs and computing nodes. The framework covers point c

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