OpenMaskDINO3D reports state-of-the-art 3D reasoning segmentation with a LISA-style SEG token and object identifiers, but uses Mask3D pseudo-labels as ground truth and lacks released code.
VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations
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We present Visual-Language Fields (VL-Fields), a neural implicit spatial representation that enables open-vocabulary semantic queries. Our model encodes and fuses the geometry of a scene with vision-language trained latent features by distilling information from a language-driven segmentation model. VL-Fields is trained without requiring any prior knowledge of the scene object classes, which makes it a promising representation for the field of robotics. Our model outperformed the similar CLIP-Fields model in the task of semantic segmentation by almost 10%.
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OpenMaskDINO3D : Reasoning 3D Segmentation via Large Language Model
OpenMaskDINO3D reports state-of-the-art 3D reasoning segmentation with a LISA-style SEG token and object identifiers, but uses Mask3D pseudo-labels as ground truth and lacks released code.