Panoptic segmentation of 3D scenes, which consists in isolating object instances in a dense 3D reconstruction of a scene, is challenging given only unposed images. Existing approaches typically extract 2D panoptic segmentations for each image using an off-the-shelf model, before optimizing an implicit geometric representation (often NeRF-based) that integrates and fuses 2D panoptic constraints. Not only this requires camera parameters and costly test-time optimization for each scene, but we argue that performing 2D panoptic segmentation despite the problem at hand being fundamentally 3D and multi-view, is likely suboptimal. In this work, we instead propose a simple integrated and unified approach. Our novel network, named PanSt3R, jointly predicts the 3D geometry and panoptic segmentation without any test-time optimization in a single forward pass. PanSt3R builds upon recent advances in 3D reconstruction, specifically upon MUSt3R, a scalable multi-view version of DUSt3R, which we entail with semantic knowledge and panoptic segmentation capabilities. We additionally revisit the standard post-processing mask merging procedure and introduce a more principled approach. Overall, the proposed PanSt3R is simple, fast and scalable. We conduct extensive experiments on multiple benchmarks and show that our method yields state of-the-art results while being orders of magnitude faster.
PanSt3R: Multi-view consistent panoptic segmentation
| Lojze Zust, Yohann Cabon, Juliette Marrie, Leonid Antsfeld, Boris Chidlovskii, Jérome Revaud, Gabriela Csurka |
| International Conference on Computer Vision (ICCV), Honolulu, Hawaii, 19-23 October, 2025 |
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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