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 676–700 of 1576 papers

TitleStatusHype
PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point CloudsCode0
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous DrivingCode0
Real-Time 3D Object Detection Using InnovizOne LiDAR and Low-Power Hailo-8 AI AcceleratorCode0
Pre-Training LiDAR-Based 3D Object Detectors Through ColorizationCode0
Joint stereo 3D object detection and implicit surface reconstructionCode0
Joint Monocular 3D Vehicle Detection and TrackingCode0
Joint 3D Proposal Generation and Object Detection from View AggregationCode0
Point-Voxel CNN for Efficient 3D Deep LearningCode0
Pragmatic Communication in Multi-Agent Collaborative PerceptionCode0
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio MaskingCode0
DeepCompress: Efficient Point Cloud Geometry CompressionCode0
IoU Loss for 2D/3D Object DetectionCode0
Inverse++: Vision-Centric 3D Semantic Occupancy Prediction Assisted with 3D Object DetectionCode0
PointFusion: Deep Sensor Fusion for 3D Bounding Box EstimationCode0
3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X OrchestrationCode0
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point CloudCode0
Prompt3D: Random Prompt Assisted Weakly-Supervised 3D Object DetectionCode0
ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object DetectionCode0
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point CloudsCode0
Improving Generalization Ability for 3D Object Detection by Learning Sparsity-invariant FeaturesCode0
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous DrivingCode0
3D Object Detection from Point Cloud via Voting Step DiffusionCode0
Three-dimensional Backbone Network for 3D Object Detection in Traffic ScenesCode0
BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using DepthCode0
Orthographic Feature Transform for Monocular 3D Object DetectionCode0
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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
2MMFusion-eNDS0.77—Unverified
3MegFusionNDS0.77—Unverified
4RacoonPowerNDS0.76—Unverified
5BEVFusion-eNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8DAANDS0.75—Unverified
9FusionVPENDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified