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
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
Polygon Detection for Room Layout Estimation using Heterogenous Graphs and Wireframes
Pytorch implementation of the ECCV 2020 paper: AtlantaNet: Inferring the 3D Indoor Layout from a Single 360 Image beyond the Manhattan World Assumption
DMH-Net Code for 3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform (ECCV 2022)
Die Hauptfunktionen von starrah/dmh-net sind: Room Layout Estimation.
Open-Source-Alternativen zu starrah/dmh-net sind unter anderem: manycore-research/spatiallm — SpatialLM is a spatial modeling framework that uses large language models to transform monocular video and sensor data… cyang0515/noncuboidroom — Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image. davidgillsjo/polygon-hgt — Polygon Detection for Room Layout Estimation using Heterogenous Graphs and Wireframes. fuenwang/led2-net — This is PyTorch implementation of our CVPR 2021 Oral paper "LED 2 -Net: Monocular 360˚ Layout Estimation via… joshua049/stereo-360-layout — This is the implementation of our paper. crs4/atlantanet — Pytorch implementation of the ECCV 2020 paper: AtlantaNet: Inferring the 3D Indoor Layout from a Single 360 Image…