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src/b3d/chisight/gen3d/inference_moves.py

@@ -384,8 +384,11 @@ def propose_vertex_color_given_visibility(
     sampled_rgb = proposed_rgbs[sampled_index]
     log_K_score = log_qs.sum() + normalized_scores[sampled_index]
 
-    ## "L proposal": given the sampled rgb, estimate the probability that
-    # it came from the one of the 3 proposals that actually was used.
+    ## "L proposal": given the sampled rgb, the L proposal proposes
+    # an index for which of the 3 proposals may have produced this sample RGB,
+    # and also proposes the other two RGB values.
+    # Here, we need to compute the logpdf of this L proposal having produced
+    # the values we sampled out of the K proposal.
     log_qs_for_this_rgb = jnp.array(
         [
             uniform.logpdf(sampled_rgb, min_rgbs1, max_rgbs1),
@@ -396,13 +399,16 @@ def propose_vertex_color_given_visibility(
     normalized_L_logprobs = normalize_log_scores(log_qs_for_this_rgb)
 
     # L score for proposing the index
-    log_L_score = normalized_L_logprobs[sampled_index]
+    log_L_score_for_index = normalized_L_logprobs[sampled_index]
 
     # Also add in the L score for proposing the other two RGB values.
     # The L proposal over these values will just generate them from their prior.
-    log_L_score += jnp.sum(log_qs) - log_qs[sampled_index]
+    log_L_score_for_unused_values = jnp.sum(log_qs) - log_qs[sampled_index]
 
-    ## Compute the overall score.
+    # full L score
+    log_L_score = log_L_score_for_index + log_L_score_for_unused_values
+
+    ## Compute the overall estimate of the marginal density of proposing `sampled_rgb`.
     overall_score = log_K_score - log_L_score
 
     ## Return