Update readme: timings

verlab · Jun 9, 2024 · 5580d38 · 5580d38
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diff --git a/README.md b/README.md
@@ -57,6 +57,19 @@ XFeat extracts a keypoint heatmap $\mathbf{K}$, a compact 64-D dense descriptor
 
 <img align="center" src="./figs/xfeat_arq.png" width=1000 />
 
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+## Timing Analyses on CPU.
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+We show that both detection branch & match refinement module costs are small and bring significant advantages in accuracy (please check the ablation section in the paper).
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+<img align="center" src="./figs/timings.png" width=840 />
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+
+Furthermore, XFeat performs effectively in both indoor and outdoor scenes, achieving an excellent compute-accuracy trade-off as demonstrated below. Note that in the paper, the teaser figure has a VGA resolution on the x-axis and 1,200 pixels on the y-axis. Below, we present an updated figure for improved clarity, maintaining the same x-y axis resolution.
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+<img align="center" src="./figs/speed_accuracy.png" width=840 />
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+
 ## Installation
 XFeat has minimal dependencies, only relying on torch. Also, XFeat does not need a GPU for real-time sparse inference (vanilla pytorch w/o any special optimization), unless you run it on high-res images. If you want to run the real-time matching demo, you will also need OpenCV.
 We recommend using conda, but you can use any virtualenv of your choice.
@@ -152,7 +165,7 @@ If you find this code useful for your research, please cite the paper:
 
 ```bibtex
 @INPROCEEDINGS{potje2024cvpr,
-  author={Guilherme {Potje} and and Felipe {Cadar} and Andre {Araujo} and Renato {Martins} and Erickson R. {Nascimento}},
+  author={Guilherme {Potje} and Felipe {Cadar} and Andre {Araujo} and Renato {Martins} and Erickson R. {Nascimento}},
   booktitle={2024 IEEE / CVF Computer Vision and Pattern Recognition (CVPR)}, 
   title={XFeat: Accelerated Features for Lightweight Image Matching}, 
   year={2024}}

diff --git a/figs/speed_accuracy.png b/figs/speed_accuracy.png
diff --git a/figs/timings.png b/figs/timings.png