ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding

arXiv:2410.13924 · cs.CV, cs.AI · Submitted 2024-10-17 · Read on arXiv

cs.CV, cs.AI

Submitted: 2024-10-17

Updated: 2025-03-20

Journal ref: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 4398-4407

DOI: 10.1109/CVPR52734.2025.00415

Code: https://github.com/Pointcept/Pointcept

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

Terminology

• Transformer — 14× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Benchmark — 11× in this paper · explained in 2 episodes
• In International Conference on Computer Vision — 9× in this paper
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Instance Segmentation — 6× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Data Augmentation — 5× in this paper · explained in 7 episodes
• European Conference on Computer Vision — 5× in this paper
• Grounding — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Abundance — 2× in this paper · explained in 1 episode
• AdamW Optimizer — 2× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• In International Conference on Learning Representations — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Segment Anything
• GPT-4 Technical Report
• Emergent Abilities of Large Language Models
• OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

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