Kavli Affiliate: Jing Wang
| First 5 Authors: Wentao Qu, Jing Wang, YongShun Gong, Xiaoshui Huang, Liang Xiao
| Summary:
Existing conditional Denoising Diffusion Probabilistic Models (DDPMs) with a
Noise-Conditional Framework (NCF) remain challenging for 3D scene understanding
tasks, as the complex geometric details in scenes increase the difficulty of
fitting the gradients of the data distribution (the scores) from semantic
labels. This also results in longer training and inference time for DDPMs
compared to non-DDPMs. From a different perspective, we delve deeply into the
model paradigm dominated by the Conditional Network. In this paper, we propose
an end-to-end robust semantic Segmentation Network based on a Conditional-Noise
Framework (CNF) of DDPMs, named CDSegNet. Specifically, CDSegNet models the
Noise Network (NN) as a learnable noise-feature generator. This enables the
Conditional Network (CN) to understand 3D scene semantics under multi-level
feature perturbations, enhancing the generalization in unseen scenes.
Meanwhile, benefiting from the noise system of DDPMs, CDSegNet exhibits strong
noise and sparsity robustness in experiments. Moreover, thanks to CNF, CDSegNet
can generate the semantic labels in a single-step inference like non-DDPMs, due
to avoiding directly fitting the scores from semantic labels in the dominant
network of CDSegNet. On public indoor and outdoor benchmarks, CDSegNet
significantly outperforms existing methods, achieving state-of-the-art
performance.
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