# Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference Source: [Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:55.159Z Authors: Aishwarya Unnikrishnan, Joey Wilson, Lu Gan, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, Maani Ghaffari Published: 2021-08-06T15:51:40.000Z ## Abstract This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model. Existence of dynamic objects in the environment can cause artifacts and traces in current mapping algorithms, leading to an inconsistent map posterior. We leverage state-of-the-art semantic segmentation and 3D flow estimation using deep learning to provide measurements for map inference. We develop a Bayesian model that propagates the scene with flow and infers a 3D continuous (i.e., can be queried at arbitrary resolution) semantic occupancy map outperforming its static counterpart. Extensive experiments using publicly available data sets show that the proposed framework improves over its predecessors and input measurements from deep neural networks consistently. ## 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. ## Original sources and implementation [Original paper]() [PDF]()