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

Showing 576600 of 1576 papers

TitleStatusHype
ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object DetectionCode1
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial RelationshipsCode1
Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single ImageCode1
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint EstimationCode1
ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image VotesCode1
Canadian Adverse Driving Conditions DatasetCode1
DSGN: Deep Stereo Geometry Network for 3D Object DetectionCode1
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object DetectionCode1
Deep Learning for 3D Point Clouds: A SurveyCode1
PointPainting: Sequential Fusion for 3D Object DetectionCode1
M3D-RPN: Monocular 3D Region Proposal Network for Object DetectionCode1
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation NetworkCode1
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty EstimationCode1
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle PerceptionCode1
Deep Hough Voting for 3D Object Detection in Point CloudsCode1
PIXOR: Real-time 3D Object Detection from Point CloudsCode1
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous DrivingCode1
PointRCNN: 3D Object Proposal Generation and Detection from Point CloudCode1
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point CloudCode1
CubeSLAM: Monocular 3D Object SLAMCode1
BirdNet: a 3D Object Detection Framework from LiDAR informationCode1
Frustum PointNets for 3D Object Detection from RGB-D DataCode1
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object DetectionCode1
YOLO9000: Better, Faster, StrongerCode1
3D Bounding Box Estimation Using Deep Learning and GeometryCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78Unverified
2MMFusion-eNDS0.77Unverified
3MegFusionNDS0.77Unverified
4RacoonPowerNDS0.76Unverified
5BEVFusion-eNDS0.76Unverified
6DeepInteraction-largeNDS0.76Unverified
7DeepInteraction-eNDS0.76Unverified
8DAANDS0.75Unverified
9FusionVPENDS0.75Unverified
10CenterPoint-FusionNDS0.75Unverified