13 dataset results for 3D Instance Segmentation

ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects. Up to now, ScanNet v2, the newest version of ScanNet, has collected 1513 annotated scans with an approximate 90% surface coverage. In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects.

1,240 PAPERS • 19 BENCHMARKS

S3DIS (Stanford 3D Indoor Scene Dataset (S3DIS))

The Stanford 3D Indoor Scene Dataset (S3DIS) dataset contains 6 large-scale indoor areas with 271 rooms. Each point in the scene point cloud is annotated with one of the 13 semantic categories.

421 PAPERS • 10 BENCHMARKS

KITTI-360

KITTI-360 is a large-scale dataset that contains rich sensory information and full annotations. It is the successor of the popular KITTI dataset, providing more comprehensive semantic/instance labels in 2D and 3D, richer 360 degree sensory information (fisheye images and pushbroom laser scans), very accurate and geo-localized vehicle and camera poses, and a series of new challenging benchmarks.

161 PAPERS • 6 BENCHMARKS

PartNet

PartNet is a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information. The dataset consists of 573,585 part instances over 26,671 3D models covering 24 object categories. This dataset enables and serves as a catalyst for many tasks such as shape analysis, dynamic 3D scene modeling and simulation, affordance analysis, and others.

123 PAPERS • 3 BENCHMARKS

SceneNN

SceneNN is an RGB-D scene dataset consisting of more than 100 indoor scenes. The scenes are captured at various places, e.g., offices, dormitory, classrooms, pantry, etc., from University of Massachusetts Boston and Singapore University of Technology and Design. All scenes are reconstructed into triangle meshes and have per-vertex and per-pixel annotation. The dataset is additionally enriched with fine-grained information such as axis-aligned bounding boxes, oriented bounding boxes, and object poses.

57 PAPERS • 1 BENCHMARK

STPLS3D

Our project (STPLS3D) aims to provide a large-scale aerial photogrammetry dataset with synthetic and real annotated 3D point clouds for semantic and instance segmentation tasks.

32 PAPERS • 3 BENCHMARKS

ScanNet200

The ScanNet200 benchmark studies 200-class 3D semantic segmentation - an order of magnitude more class categories than previous 3D scene understanding benchmarks. The source of scene data is identical to ScanNet, but parses a larger vocabulary for semantic and instance segmentation

22 PAPERS • 3 BENCHMARKS

CREMI

MICCAI Challenge on Circuit Reconstruction from Electron Microscopy Images.

4 PAPERS • 1 BENCHMARK

FOR-instance (FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees)

The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications. While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. The lack of suitable benchmark datasets and reliance on small datasets have limited method development. The emergence of deep learning models exacerbates the need for standardized benchmarks. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation methods for forested 3D scenes.

3 PAPERS • NO BENCHMARKS YET

MitoEM

Contains mitochondria instances.

3 PAPERS • 1 BENCHMARK

Fraunhofer EZRT XXL-CT Instance Segmentation Me163

The 'Me 163' was a Second World War fighter airplane and a result of the German air force secret developments. One of these airplanes is currently owned and displayed in the historic aircraft exhibition of the 'Deutsches Museum' in Munich, Germany. To gain insights with respect to its history, design and state of preservation, a complete CT scan was obtained using an industrial XXL-computer tomography scanner at Fraunhofer EZRT .

1 PAPER • NO BENCHMARKS YET

XA Bin-Picking

XA Bin-Picking is a point-cloud dataset comprising both simulated and real-world scenes with three industrial parts. Synthesized scenes consists of 1000 training samples. The test samples are real scenes and the ground truth instance labels are made manually. There are 20 to 30 identical types of parts randomly piled up in a scene. Each scene contains about 60,000 boundary points. Each point in the scene has instance annotations. The parts are texture-less and have no discernible color. Both of training samples and test sam- ples only contain the boundary points of parts.

1 PAPER • NO BENCHMARKS YET

InfiniteRep

InfiniteRep is a synthetic, open-source dataset for fitness and physical therapy (PT) applications. It includes 1k videos of diverse avatars performing multiple repetitions of common exercises. It includes significant variation in the environment, lighting conditions, avatar demographics, and movement trajectories. From cadence to kinematic trajectory, each rep is done slightly differently -- just like real humans. InfiniteRep videos are accompanied by a rich set of pixel-perfect labels and annotations, including frame-specific repetition counts.

0 PAPER • NO BENCHMARKS YET

Datasets

13 dataset results for 3D Instance Segmentation