# Pixel Consensus Voting for Panoptic Segmentation Source: [Pixel Consensus Voting for Panoptic Segmentation]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:11:48.934Z Authors: Haochen Wang, Ruotian Luo, Michael Maire, Greg Shakhnarovich Published: 2020-04-04T04:33:45.000Z ## Abstract The core of our approach, Pixel Consensus Voting, is a framework for instance segmentation based on the Generalized Hough transform. Pixels cast discretized, probabilistic votes for the likely regions that contain instance centroids. At the detected peaks that emerge in the voting heatmap, backprojection is applied to collect pixels and produce instance masks. Unlike a sliding window detector that densely enumerates object proposals, our method detects instances as a result of the consensus among pixel-wise votes. We implement vote aggregation and backprojection using native operators of a convolutional neural network. The discretization of centroid voting reduces the training of instance segmentation to pixel labeling, analogous and complementary to FCN-style semantic segmentation, leading to an efficient and unified architecture that jointly models things and stuff. We demonstrate the effectiveness of our pipeline on COCO and Cityscapes Panoptic Segmentation and obtain competitive results. Code will be open-sourced. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Panoptic Segmentation on COCO (test-dev)](): Panoptic Segmentation; COCO (test-dev); PQ: 37.7. - [Panoptic Segmentation on COCO (val)](): Panoptic Segmentation; COCO (val); PQ: 37.5. - [Panoptic Segmentation on Cityscapes (val)](): Panoptic Segmentation; Cityscapes (val); PQ: 54.2. ## Original sources and implementation [Original paper]() [PDF]()