alexklwong/adaframe-depth-completion
PyTorch implementation of An Adaptive Framework for Learning Unsupervised Depth Completion (RAL 2021 & ICRA 2021)
This project helps robotics engineers and computer vision researchers generate highly detailed 3D scene data. It takes an RGB image and sparse 3D depth measurements (from sensors like lidar or computational methods like Structure-from-Motion) and produces a dense, complete 3D depth map. This is useful for applications requiring a full understanding of a scene's geometry, such as autonomous navigation or 3D reconstruction.
No commits in the last 6 months.
Use this if you need to create accurate and complete 3D depth maps from images where only partial depth information is available, especially in robotics or autonomous systems.
Not ideal if your application requires only rough depth estimates or if you primarily work with single-image depth prediction without sparse depth inputs.
Stars
10
Forks
1
Language
—
License
—
Category
Last pushed
Jun 01, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/alexklwong/adaframe-depth-completion"
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...