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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
Polygon Detection for Room Layout Estimation using Heterogenous Graphs and Wireframes
This is PyTorch implementation of our CVPR 2021 Oral paper "LED 2 -Net: Monocular 360˚ Layout Estimation via Differentiable Depth Rendering".
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
Pytorch implementation of the ECCV 2020 paper: AtlantaNet: Inferring the 3D Indoor Layout from a Single 360 Image beyond the Manhattan World Assumption
The main features of crs4/atlantanet are: Room Layout Estimation.
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… 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. justacar/plane-dust3r — This repository contains the official implementation of the paper "Unposed Sparse Views Room Layout Reconstruction in… cyang0515/noncuboidroom — Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image.