Papers with Code · CC BY-SA 4.0

Training-free 3D Point Cloud Classification

13 평가 기록 2 datasets

Evaluation on target datasets for 3D Point Cloud Classification without any training

논문

CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention

2209.14169 2022-09-28 이 페이지의 평가 2건

Accuracy (%): 21.5Need 3D Data?: No

CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training

2210.01055 2022-10-03 이 페이지의 평가 2건

Accuracy (%): 49.4Need 3D Data?: Yes

Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis

2303.08134 2023-03-14 이 페이지의 평가 2건

Accuracy (%): 82.6Parameters: 0MNeed 3D Data?: Yes

Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification

2412.03056 2024-12-04 이 페이지의 평가 2건

Accuracy (%): 85.3Parameters: 0MNeed 3D Data?: Yes

PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning

2211.11682 2022-11-21 이 페이지의 평가 2건

Accuracy (%): 64.2Need 3D Data?: No

PointCLIP: Point Cloud Understanding by CLIP

2112.02413 2021-12-04 이 페이지의 평가 2건

Accuracy (%): 20.2Need 3D Data?: No

ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding

2212.05171 2022-12-10 이 페이지의 평가 1건

Accuracy (%): 60.4Need 3D Data?: Yes