SCENE-Net V2: Interpretable Multiclass 3D Scene Understanding with Geometric Priors

In this paper, we present SCENE-Net V2, a new resource-efficient, textbfgray-box model for multiclass 3D scene understanding. SCENE-Net V2 leverages Group Equivariant Non-Expansive Operators (GENEOs) to incorporate fundamental geometric priors as inductive biases, offering a more transparent alternative to the prevalent black-box models in the domain. This model addresses the limitations of its white-box predecessor, SCENE-Net, by expanding its applicability from pole-like structures to a wider range of datasets with detailed 3D elements. Our model achieves the sweet-spot between application and transparency: SCENE-Net V2 is a general method for object identification with interpretability guarantees. Our experimental results demonstrate that SCENE-Net V2 achieves competitive performance with a significantly lower parameter count. Furthermore, we propose the use of GENEO-based architectures as a feature extraction tool for black-box models, enabling an increase in performance by adding a minimal number of meaningful parameters. Our code is available in: https://github.com/dlavado/SCENE-Net-V2

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Bibtex:

@inproceedings{Lavado2024scene-net-v2,
    author = "Lavado, Diogo Mateus and Soares, Claudia and Micheletti, Alessandra",
    title = "SCENE-Net V2: Interpretable Multiclass 3D Scene Understanding with Geometric Priors",
    booktitle = "ICML 2024 Workshop GRaM",
    url = "https://openreview.net/forum?id=6pKRn6tttu",
    year = "2024",
    eprint = "https://openreview.net/pdf?id=lavado|scenenet\\_v2\\_interpretable\\_multiclass\\_3d\\_scene\\_understanding\\_with\\_geometric\\_priors",
    organization = "ICML.cc/2024/Workshop/GRaM",
    abstract = "In this paper, we present SCENE-Net V2, a new resource-efficient, \textbf{gray-box model} for multiclass 3D scene understanding. SCENE-Net V2 leverages Group Equivariant Non-Expansive Operators (GENEOs) to incorporate fundamental geometric priors as inductive biases, offering a more transparent alternative to the prevalent black-box models in the domain. This model addresses the limitations of its white-box predecessor, SCENE-Net, by expanding its applicability from pole-like structures to a wider range of datasets with detailed 3D elements. Our model achieves the sweet-spot between application and transparency: SCENE-Net V2 is a general method for object identification with interpretability guarantees. Our experimental results demonstrate that SCENE-Net V2 achieves competitive performance with a significantly lower parameter count. Furthermore, we propose the use of GENEO-based architectures as a feature extraction tool for black-box models, enabling an increase in performance by adding a minimal number of meaningful parameters. Our code is available in: https://github.com/dlavado/SCENE-Net-V2"
}