ImmersiveIML – Immersive interactive machine learning for 3D point cloud classification: the neural network at your fingertips

Maxime Cordeil (1) , Thomas Billy (1) , Nicolas Mellado (2, 3) , Loic Barthe (3, 4) , Nadine Couture (5) , Patrick Reuter (6, 7)

1 UQ [All campuses : Brisbane, Dutton Park Gatton, Herston, St Lucia and other locations] - The University of Queensland
2 CNRS - Centre National de la Recherche Scientifique
3 IRIT-STORM - Structural Models and Tools in Computer Graphics
4 UT3 - Université Toulouse III - Paul Sabatier
5 ESTIA INSTITUTE OF TECHNOLOGY
6 LaBRI - Laboratoire Bordelais de Recherche en Informatique
7 MANAO - Melting the frontiers between Light, Shape and Matter

We propose an initial exploration of an interactive machine-learning (IML) dialogue in immersive, interactive Virtual Reality (VR) for the classification of points in 3D point clouds. We contribute ImmersiveIML, an Immersive Analytics tool which builds on humanmachine learning trial-and-error dialogue to support an iterative classification process of points in the 3D point cloud. The interactions in ImmersiveIML are designed to be both expressive and minimal; we designed the iterative process to be supported by (1) 6-DOF controllers to allow direct interaction in large 3D point clouds via a minimal brushing technique, (2) a fast trainable machine learning model, and (3) the direct visual feedback of classification results. This constitutes an iterative human-in-the-loop process that eventually converges to a classification according to human intent. We argue that this approach is a novel contribution that supports a constant improvement of the classification model and fast tracks classification tasks with this type of data, in an immersive scenario. We report on the design and implementation of ImmersiveIML and demonstrate its capabilities with two emblematic application scenarios: edge detection in 3D and classification of trees in a city LiDAR dataset.

Mots clés: Gestural input; Interaction paradigms; Virtual reality; Computing methodologies; Neural networks; Human-centered computing; Visualization; Visu- alization techniques; Interaction techniques

Domaines: Interface homme-machine [cs.HC]; Intelligence artificielle [cs.AI]; Synthèse d'image et réalité virtuelle [cs.GR]

Maxime Cordeil, Thomas Billy, Nicolas Mellado, Loic Barthe, Nadine Couture, et al.. ImmersiveIML – Immersive interactive machine learning for 3D point cloud classification: the neural network at your fingertips. IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct 2023), Oct 2023, Sydney, Australia. pp.81-85, ⟨10.1109/ISMAR-Adjunct60411.2023.00025⟩. ⟨hal-04246601⟩