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

TL;DR

This paper introduces 4DContrast, a contrastive learning method that incorporates dynamic 4D object priors into 3D representations through unsupervised pre-training, enhancing scene understanding tasks.

Contribution

It proposes a novel data augmentation and contrastive learning framework that encodes 4D invariances into 3D representations for improved scene understanding.

Findings

Improves 3D semantic segmentation accuracy

Enhances object detection performance

Boosts results in data-scarce scenarios

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.