MS: Added perpetua project

Author: MikeS96

_bibliography/papers.bib

@@ -601,3 +601,14 @@ @inproceedings{liu2023meshdiffusion
   arxiv = {2303.08133},
   abstract = {We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are more desirable in practice, because (1) they enable easy and arbitrary manipulation of shapes for relighting and simulation, and (2) they can fully leverage the power of modern graphics pipelines which are mostly optimized for meshes. Previous scalable methods for generating meshes typically rely on sub-optimal post-processing, and they tend to produce overly-smooth or noisy surfaces without fine-grained geometric details. To overcome these shortcomings, we take advantage of the graph structure of meshes and use a simple yet very effective generative modeling method to generate 3D meshes. Specifically, we represent meshes with deformable tetrahedral grids, and then train a diffusion model on this direct parametrization. We demonstrate the effectiveness of our model on multiple generative tasks.}
 }
+
+@inproceedings{saavedra2025perpetua,
+  title={Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments},
+  author={Saavedra-Ruiz, Miguel and Nashed, Samer and Gauthier, Charlie and Paull, Liam},
+  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
+  year={2025},
+  month={Oct},
+  image = {papers/perpetua.gif},
+  projectpage={https://montrealrobotics.ca/perpetua/},
+  abstract = {Many robotic systems require extended deployments in complex, dynamic environments. In such deployments, parts of the environment may change in between subsequent observations by the robot. Few robotic mapping or environment modeling algorithms are capable of representing dynamic features in a way that enables predicting their future state. Instead, most approaches opt to filter certain state observations, either by removing them or some form of weighted averaging. This paper introduces Perpetua, a method for modeling the dynamics of semi-static features. Perpetua is able to: incorporate prior knowledge about the dynamics of the feature if it exists, track multiple hypotheses, and adapt over time to enable predicting their future state. Specifically, we chain together mixtures of "persistence" and "emergence" filters to model the probability that features will disappear or reappear in a formal Bayesian framework. The approach is an efficient, scalable, general, and robust method for estimating the state of features in an environment, both in the present as well as at arbitrary future times. Through experiments on simulated and real-world data, we find Perpetua yields better accuracy than similar approaches while also being online adaptable and robust to missing observations.}
+}

_projects/perpetua.md

@@ -0,0 +1,24 @@
+---
+title: Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments
+# status: active
+
+notitle: false
+
+description: |
+  An efficient method to estimate and predict feature persistence in semi-static environments using a mixture formulation that is online adapatable and robust to missing observations.
+
+people:
+  - miguel
+  - samer
+  - charlie2
+  - liam
+
+layout: project
+image: /img/papers/perpetua.gif
+link: https://montrealrobotics.ca/perpetua/
+last-updated: 2025-06-26
+---
+
+## One-4-All: Neural Potential Fields for Embodied Navigation
+
+Many robotic systems require extended deployments in complex, dynamic environments. In such deployments, parts of the environment may change in between subsequent observations by the robot. Few robotic mapping or environment modeling algorithms are capable of representing dynamic features in a way that enables predicting their future state. Instead, most approaches opt to filter certain state observations, either by removing them or some form of weighted averaging. This paper introduces Perpetua, a method for modeling the dynamics of semi-static features. Perpetua is able to: incorporate prior knowledge about the dynamics of the feature if it exists, track multiple hypotheses, and adapt over time to enable predicting their future state. Specifically, we chain together mixtures of "persistence" and "emergence" filters to model the probability that features will disappear or reappear in a formal Bayesian framework. The approach is an efficient, scalable, general, and robust method for estimating the state of features in an environment, both in the present as well as at arbitrary future times. Through experiments on simulated and real-world data, we find Perpetua yields better accuracy than similar approaches while also being online adaptable and robust to missing observations.