HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition

Figure 1. We propose HOTSPOT, a neural signed distance function optimization method that establishes an asymptotic sufficient condition to guarantee convergence to a true distance function, enabling precise surface reconstruction and level set representation for complex shapes. Here we show a reconstruction from a point cloud sampled from the reference bunny (taken from Mehta et al. [1]) on the right. In the inset, we visualize the recovered signed distance function on a horizontal slice, using warm colors for positive values and cool for negative (zoom in for details). Our reconstruction is significantly more accurate than prior works (SAL [2], DiGS [3], and StEik [4]).