Papers with Code 아카이브

3D Geometry Perception

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
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Image: Zhao et al

데이터셋

출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.

OmniObject3D

OmniObject3D is a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. OmniObject3D has several appealing properties: 1) Large Vocabulary: It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets (e.g., ImageNet and LVIS), benefiting the pursuit of generalizable 3D representations. 2) Rich Annotations: Each 3D object is captured with both 2D and 3D sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos. 3) Realistic Scans: The professional scanners support highquality object scans with precise shapes and realistic appearances.

FEE Corridor

The data set contains point cloud data captured in an indoor environment with precise localization and ground truth mapping information. Two ”stop-and-go” data sequences of a robot with mounted Ouster OS1-128 lidar are provided. This data-capturing strategy allows recording lidar scans that do not suffer from an error caused by sensor movement. Individual scans from static robot positions are recorded. Additionally, point clouds recorded with the Leica BLK360 scanner are provided as mapping ground-truth data.

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.