hongxiaoy/ISO
[ECCV 2024] Monocular Occupancy Prediction for Scalable Indoor Scenes
This project helps professionals in robotics, virtual reality, or architectural visualization create detailed 3D models of indoor spaces from just a single 2D image. It takes an ordinary camera image of a room and generates a full 3D occupancy map, labeling objects like chairs, tables, and walls, even for parts of the scene not visible in the original photo. This tool is for researchers and developers working on AI models that need to understand and reconstruct indoor environments.
No commits in the last 6 months.
Use this if you need to generate a complete 3D reconstruction of an indoor scene, including hidden surfaces and object classifications, using only a single input image.
Not ideal if you require real-time 3D reconstruction for highly dynamic environments or if you don't have access to GPU hardware for processing.
Stars
66
Forks
1
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 24, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/hongxiaoy/ISO"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
vita-epfl/monoloco
A 3D vision library from 2D keypoints: monocular and stereo 3D detection for humans, social...
fangchangma/self-supervised-depth-completion
ICRA 2019 "Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and...
nburrus/stereodemo
Small Python utility to compare and visualize the output of various stereo depth estimation algorithms
JiawangBian/sc_depth_pl
SC-Depth (V1, V2, and V3) for Unsupervised Monocular Depth Estimation ...
wvangansbeke/Sparse-Depth-Completion
Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st...