LangOcc: Self-Supervised Open Vocabulary Occupancy Estimation via Volume Rendering

• URL: http://arxiv.org/abs/2407.17310v2
• Date: Thu, 25 Jul 2024 07:47:11 GMT
• Title: LangOcc: Self-Supervised Open Vocabulary Occupancy Estimation via Volume Rendering
• Authors: Simon Boeder, Fabian Gigengack, Benjamin Risse,
• Abstract summary: LangOcc is a novel approach for open vocabulary occupancy estimation. It is trained only via camera images, and can detect arbitrary semantics via vision-language alignment. We achieve state-of-the-art results in self-supervised semantic occupancy estimation on the Occ3D-nuScenes dataset.
• Score: 0.5852077003870417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The 3D occupancy estimation task has become an important challenge in the area of vision-based autonomous driving recently. However, most existing camera-based methods rely on costly 3D voxel labels or LiDAR scans for training, limiting their practicality and scalability. Moreover, most methods are tied to a predefined set of classes which they can detect. In this work we present a novel approach for open vocabulary occupancy estimation called LangOcc, that is trained only via camera images, and can detect arbitrary semantics via vision-language alignment. In particular, we distill the knowledge of the strong vision-language aligned encoder CLIP into a 3D occupancy model via differentiable volume rendering. Our model estimates vision-language aligned features in a 3D voxel grid using only images. It is trained in a self-supervised manner by rendering our estimations back to 2D space, where ground-truth features can be computed. This training mechanism automatically supervises the scene geometry, allowing for a straight-forward and powerful training method without any explicit geometry supervision. LangOcc outperforms LiDAR-supervised competitors in open vocabulary occupancy by a large margin, solely relying on vision-based training. We also achieve state-of-the-art results in self-supervised semantic occupancy estimation on the Occ3D-nuScenes dataset, despite not being limited to a specific set of categories, thus demonstrating the effectiveness of our proposed vision-language training.

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