SERVE: Semantic Evidence from Return Verification for LiDAR Segmentation

An unfamiliar object can receive a confident semantic label when a LiDAR model only compares known classes. We propose Semantic Evidence from Return Verification (SERVE), which combines appearance support with class-conditioned range compatibility. A full-scan encoder supplies appearance support, while a separate predictor excludes the target angular cell and produces a distribution over log range for each class. Its density at the recorded range enters the supervised class decision alongside appearance support. The strongest joint class support supplies both the semantic label and the unknown score. Training uses normal semantic labels and recorded ranges. On STU, SERVE reaches 73.94% validation and 61.74% test average precision, compared with 21.36% and 15.09% for a matched appearance model, while improving normal semantic mIoU by 1.24 points. At a 1% normal false-positive rate, it recovers 81.6% of unknown points confidently assigned to known classes by the appearance model. Matched ablations assess the contributions of target exclusion, class-conditioned measurement support, and joint semantic training. These results support integrating learned range compatibility into semantic recognition.