SenseFuse: Label-Free Fusion of Image and Shape Encoders for Open-Vocabulary 3D Instance Segmentation

arXiv:2609.20475 · cs.CV, cs.RO · Submitted 2026-09-17 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: 8 pages, 6 figures. Code: https://github.com/hanes1207/SenseFuse

Code: https://github.com/hanes1207/SenseFuse

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 33× in this paper · explained in 1 episode
• Instance Segmentation — 14× in this paper · explained in 3 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Point Clouds — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Distillation — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Pseudo-labeling — 2× in this paper · explained in 3 episodes
• Pseudo-labels — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Average Precision — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• European Conference on Computer Vision — 1× in this paper
• Fidelity — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 1× in this paper
• Korea Advanced Institute of Science and Technology — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Training-Free Framework — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• The Replica Dataset: A Digital Replica of Indoor Spaces
• EVA-CLIP: Improved Training Techniques for CLIP at Scale

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