SOTAVerified

3D Object Detection

3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.

( Image credit: AVOD )

Papers

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Showing 1526–1550 of 1576 papers

TitleStatusHype
3D-SIS: 3D Semantic Instance Segmentation of RGB-D ScansCode0
IPOD: Intensive Point-based Object Detector for Point Cloud—0
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape—0
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data—0
Joint Monocular 3D Vehicle Detection and TrackingCode0
MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object LocalizationCode0
Orthographic Feature Transform for Monocular 3D Object DetectionCode0
RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement—0
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose EstimationCode0
Focal Loss in 3D Object DetectionCode0
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery—0
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic ImageryCode0
Holistic 3D Scene Parsing and Reconstruction from a Single RGB ImageCode0
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point CloudCode0
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation—0
Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulk—0
3D Object Detection With Latent Support Surfaces—0
Multi-Level Fusion Based 3D Object Detection From Monocular Images—0
Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System—0
Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation—0
A General Pipeline for 3D Detection of Vehicles—0
SBNet: Sparse Blocks Network for Fast InferenceCode0
Joint 3D Proposal Generation and Object Detection from View AggregationCode0
PointFusion: Deep Sensor Fusion for 3D Bounding Box EstimationCode0
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance SegmentationCode0
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All datasetsnuScenesScanNetV2SUN-RGBD valKITTI Cars EasyKITTI Cars HardnuScenes Camera OnlyKITTI Cars Easy valKITTI Cars Moderate valnuscenes Camera-RadarView-of-Delft (val)KITTI Cars Hard valDAIR-V2X-I

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MegFusionNDS0.77—Unverified
3MMFusion-eNDS0.77—Unverified
4BEVFusion-eNDS0.76—Unverified
5RacoonPowerNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8FusionVPENDS0.75—Unverified
9FocalFormer3D-FNDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified