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Found 3 papers, 3 papers with code
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast
Geometry and color information provided by the point clouds are both crucial for 3D scene understanding.
Ranked #1 on
Unsupervised 3D Semantic Segmentation
on ScanNetV2
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms.
Ranked #1 on
Zero-Shot Transfer 3D Point Cloud Classification
on ModelNet10
(using extra training data)
3D Point Cloud Linear Classification
Few-Shot 3D Point Cloud Classification
+2
Language-Assisted 3D Feature Learning for Semantic Scene Understanding
To guide 3D feature learning toward important geometric attributes and scene context, we explore the help of textual scene descriptions.
