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Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
Torch implementation of our CVPR 18 paper: "LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image"
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
Pytorch implementation of the ECCV 2020 paper: AtlantaNet: Inferring the 3D Indoor Layout from a Single 360 Image beyond the Manhattan World Assumption
This is the implementation of our CVPRW'21 paper " OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas " accepted at the 2nd workshop on Omnidirectional Computer Vision.
The main features of rshivansh/omnilayout are: Room Layout Estimation, Scene Understanding.
Projects with overlapping indexed features include: cyang0515/noncuboidroom — Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image. zouchuhang/layoutnet — Torch implementation of our CVPR 18 paper: "LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image". manycore-research/spatiallm — SpatialLM is a spatial modeling framework that uses large language models to transform monocular video and sensor data… crs4/atlantanet — Pytorch implementation of the ECCV 2020 paper: AtlantaNet: Inferring the 3D Indoor Layout from a Single 360 Image… drprojects/deepviewagg — Official implementation for Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation… davidgillsjo/polygon-hgt — Polygon Detection for Room Layout Estimation using Heterogenous Graphs and Wireframes.