Torch implementation of our CVPR 18 paper: "LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image"
zouchuhang/layoutnet 的主要功能包括：Computer Vision Research, Room Layout Estimation, Scene Understanding。
zouchuhang/layoutnet 的开源替代品包括： cyang0515/noncuboidroom — Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image. rshivansh/omnilayout — This is the implementation of our CVPRW'21 paper " OmniLayout: Room Layout Reconstruction from Indoor Spherical… manycore-research/spatiallm — SpatialLM is a spatial modeling framework that uses large language models to transform monocular video and sensor data… zalandoresearch/fashion-mnist — This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy… akanazawa/cmr — Angjoo Kanazawa \ , Shubham Tulsiani \ , Alexei A. Efros, Jitendra Malik. agrimgupta92/sgan — Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018.
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
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.
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
This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and