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Valentin Bieri

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2 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

HouseLayout3D: A Benchmark and Training-free Baseline for 3D Layout Estimation in the Wild

Valentin Bieri
Marie-Julie Rakotosaona
Keisuke Tateno
Francis Engelmann
Leonidas Guibas

Current 3D layout estimation models are predominantly trained on synthetic datasets biased toward simplistic, single-floor scenes. This prevents them from generalizing to complex, multi-floor buildings, often forcing a per-floor processing approach that sacrifices global context. Few works have attempted to holistically address multi-floor layouts. In this work, we introduce HouseLayout3D, a real-world benchmark dataset, which highlights the limitations of existing research when handling expansive, architecturally complex spaces. Additionally, we propose MultiFloor3D, a baseline method leveraging recent advances in 3D reconstruction and 2D segmentation. Our approach significantly outperforms state-of-the-art methods on both our new and existing datasets. Remarkably, it does not require any layout-specific training.

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UAI Conference 2023 Conference Paper

BeliefPPG: Uncertainty-aware heart rate estimation from PPG signals via belief propagation

Valentin Bieri
Paul Streli
Berken Utku Demirel
Christian Holz 0001

We present a novel learning-based method that achieves state-of-the-art performance on several heart rate estimation benchmarks extracted from photoplethysmography signals (PPG). We consider the evolution of the heart rate in the context of a discrete-time stochastic process that we represent as a hidden Markov model. We derive a distribution over possible heart rate values for a given PPG signal window through a trained neural network. Using belief propagation, we incorporate the statistical distribution of heart rate changes to refine these estimates in a temporal context. From this, we obtain a quantized probability distribution over the range of possible heart rate values that captures a meaningful and well-calibrated estimate of the inherent predictive uncertainty. We show the robustness of our method on eight public datasets with three different cross-validation experiments.

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