Fig. 3: Comparison summary of our method with state-of-the-art methods. | Nature

Fig. 3: Comparison summary of our method with state-of-the-art methods.

From: Low-latency automotive vision with event cameras

Fig. 3

a,b, Comparison of asynchronous, dense feedforward and dense recurrent methods, in terms of task performance (mAP) and computational complexity (MFLOPS per inserted event) on the purely event-based Gen1 detection dataset41 (a) and N-Caltech101 (ref. 42) (b). c, Results of DSEC-Detection. All methods on this benchmark use images and events and are tasked to predict labels 50 ms after the first image, using events. Methods with dagger symbol use directed voxel grid pooling. For a full table of results, see Extended Data Table 1.

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