metadata
license: apache-2.0
tags:
- 3d-object-detection
- multi-object-tracking
- gaussian-splatting
- camera-radar-fusion
- nuscenes
datasets:
- nuscenes
GaussianMOT
Joint 3D object detection and tracking on a camera–radar Gaussian BEV. Code: https://github.com/KimJunHan/GaussianMOT
This repository holds the checkpoint reported in the paper (weights only, end of the 24th training epoch).
| File | Size | SHA-256 |
|---|---|---|
gaussianmot_epoch23.ckpt |
368 MB | d3e3a5e55265b878420dbbcf1d6868e47350e8c968fb5061e2aad3b88af5dd23 |
Results (nuScenes val, inference seed 0)
| Detection | Tracking | ||
|---|---|---|---|
| mAP | 35.0 | AMOTA | 0.348 |
| NDS | 42.5 | AMOTP | 1.31 |
| mATE | 0.539 | Recall | 0.411 |
| mASE | 0.307 | ID-S | 897 |
| mAOE | 0.681 | ||
| mAVE | 0.511 | ||
| mAAE | 0.465 |
6 cameras + 5 radars, 448×800 input, 6.5 FPS on an RTX 3090 (batch size 1).
Usage
git clone https://github.com/KimJunHan/GaussianMOT && cd GaussianMOT
hf download kimjunhan/GaussianMOT gaussianmot_epoch23.ckpt --local-dir ckpt
python tools/evaluate.py checkpoint_path=ckpt/gaussianmot_epoch23.ckpt device=cuda
The file contains state_dict only (no optimizer state); it loads with
tools/evaluate.py as is. Densification draws random shifts at inference, so
results depend on eval_seed (default 0) in the last digit.