JiayuZou2020/DiffBEV
Official PyTorch implementation for a conditional diffusion probability model in BEV perception
DiffBEV helps autonomous driving engineers create more accurate bird's eye view (BEV) representations of a vehicle's surroundings. It takes in raw camera images and LiDAR scans, which often contain noise, and processes them to output a cleaner, more comprehensive BEV semantic map and 3D object detection. This improves the foundational data used for crucial tasks like vehicle planning and motion prediction.
255 stars. No commits in the last 6 months.
Use this if you need to improve the accuracy and robustness of BEV perception data for autonomous vehicle systems, especially when dealing with noisy sensor inputs.
Not ideal if you are looking for a general-purpose image processing tool or if your primary goal is not related to autonomous driving BEV perception.
Stars
255
Forks
14
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 04, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/JiayuZou2020/DiffBEV"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
xie-lab-ml/Golden-Noise-for-Diffusion-Models
[ICCV2025] The code of our work "Golden Noise for Diffusion Models: A Learning Framework".
yulewang97/ERDiff
[NeurIPS 2023 Spotlight] Official Repo for "Extraction and Recovery of Dpatio-temporal Structure...
UNIC-Lab/RadioDiff
This is the code for the paper "RadioDiff: An Effective Generative Diffusion Model for...
pantheon5100/pid_diffusion
This repository is the official implementation of the paper: Physics Informed Distillation for...
zju-pi/diff-sampler
An open-source toolbox for fast sampling of diffusion models. Official implementations of our...