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@@ -1041,6 +1041,8 @@ Even though the LeNet-inspired encoder proved capable of achieving our degradati
 
 The receptive field of a convolutional neural network describes the region of the input that influences a single output activation. Its size and aspect ratio determine which structures the network can effectively capture: if the RF is too small, larger patterns cannot be detected, while an excessively large RF may blur fine details. For standard image data, the RF is often expressed as a symmetric $n \times n$ region \todo[inline]{add schematic of square RF}, but in principle it can be computed independently per axis.
 
+\fig{setup_rf_concept}{diagrams/rf_figure}{UNFINISHED - rf concept}
+
 In the case of spherical LiDAR projections, the input has a highly unbalanced resolution due to the sensor geometry. A fixed number of vertical channels (typically 32--128) sweeps across the horizontal axis, producing thousands of measurements per channel. This results in a pixel-per-degree resolution of approximately $0.99^{\circ}$/pixel vertically and $0.18^{\circ}$/pixel horizontally \todo[inline]{double-check with calculation graphic/table}. Consequently, the LeNet-inspired encoder’s calculated receptive field of $16 \times 16$ pixels translates to an angular size of $15.88^{\circ} \times 2.81^{\circ}$, which is highly rectangular in angular space. Such a mismatch risks limiting the network’s ability to capture degradation patterns that extend differently across the two axes. \todo[inline]{add schematic showing rectangular angular RF overlaid on LiDAR projection}
 
 To address this, we developed an efficient network architecture with asymmetric convolution kernels, resulting in a receptive field of $10 \times 52$ pixels. In angular terms, this corresponds to $9.93^{\circ} \times 9.14^{\circ}$, which is far more balanced between vertical and horizontal directions. This adjustment increases the likelihood of capturing a broader variety of degradation patterns. Additional design improvements were incorporated as well, which will be described in the following section.