4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding

• URL: http://arxiv.org/abs/2112.02990v1
• Date: Mon, 6 Dec 2021 13:09:07 GMT
• Title: 4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding
• Authors: Yujin Chen, Matthias Nie{\ss}ner, Angela Dai
• Abstract summary: We present a new approach to instill 4D dynamic object priors into learned 3D representations by unsupervised pre-training. We propose a new data augmentation scheme leveraging synthetic 3D shapes moving in static 3D environments. Experiments demonstrate that our unsupervised representation learning results in improvement in downstream 3D semantic segmentation, object detection, and instance segmentation tasks.
• Score: 22.896937940702642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new approach to instill 4D dynamic object priors into learned 3D representations by unsupervised pre-training. We observe that dynamic movement of an object through an environment provides important cues about its objectness, and thus propose to imbue learned 3D representations with such dynamic understanding, that can then be effectively transferred to improved performance in downstream 3D semantic scene understanding tasks. We propose a new data augmentation scheme leveraging synthetic 3D shapes moving in static 3D environments, and employ contrastive learning under 3D-4D constraints that encode 4D invariances into the learned 3D representations. Experiments demonstrate that our unsupervised representation learning results in improvement in downstream 3D semantic segmentation, object detection, and instance segmentation tasks, and moreover, notably improves performance in data-scarce scenarios.

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