Fast and Efficient: Mask Neural Fields for 3D Scene Segmentation

• URL: http://arxiv.org/abs/2407.01220v2
• Date: Mon, 25 Nov 2024 03:15:56 GMT
• Title: Fast and Efficient: Mask Neural Fields for 3D Scene Segmentation
• Authors: Zihan Gao, Lingling Li, Licheng Jiao, Fang Liu, Xu Liu, Wenping Ma, Yuwei Guo, Shuyuan Yang,
• Abstract summary: This paper presents MaskField, which enables efficient 3D open-vocabulary segmentation with neural fields from a novel perspective. MaskField decomposes the distillation of mask and semantic features from foundation models by formulating a mask feature field and queries. Our experiments show that MaskField not only surpasses prior state-of-the-art methods but also achieves remarkably fast convergence.
• Score: 47.08813064337934
• License:
• Abstract: Understanding 3D scenes is a crucial challenge in computer vision research with applications spanning multiple domains. Recent advancements in distilling 2D vision-language foundation models into neural fields, like NeRF and 3DGS, enable open-vocabulary segmentation of 3D scenes from 2D multi-view images without the need for precise 3D annotations. However, while effective, these methods typically rely on the per-pixel distillation of high-dimensional CLIP features, introducing ambiguity and necessitating complex regularization strategies, which adds inefficiency during training. This paper presents MaskField, which enables efficient 3D open-vocabulary segmentation with neural fields from a novel perspective. Unlike previous methods, MaskField decomposes the distillation of mask and semantic features from foundation models by formulating a mask feature field and queries. MaskField overcomes ambiguous object boundaries by naturally introducing SAM segmented object shapes without extra regularization during training. By circumventing the direct handling of dense high-dimensional CLIP features during training, MaskField is particularly compatible with explicit scene representations like 3DGS. Our extensive experiments show that MaskField not only surpasses prior state-of-the-art methods but also achieves remarkably fast convergence. We hope that MaskField will inspire further exploration into how neural fields can be trained to comprehend 3D scenes from 2D models.

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