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 351–375 of 1576 papers

TitleStatusHype
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial AttacksCode1
Learning Object-level Point Augmentor for Semi-supervised 3D Object DetectionCode1
Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy CommunicationCode1
ConQueR: Query Contrast Voxel-DETR for 3D Object DetectionCode1
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving ScenariosCode1
Towards Accurate Ground Plane Normal Estimation from Ego-MotionCode1
SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point CloudCode1
MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object DetectionCode1
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object DetectionCode1
Superpoint Transformer for 3D Scene Instance SegmentationCode1
3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection TransformersCode1
3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object DetectionCode1
Sparse2Dense: Learning to Densify 3D Features for 3D Object DetectionCode1
AeDet: Azimuth-invariant Multi-view 3D Object DetectionCode1
aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range PerceptionCode1
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object DetectionCode1
3D Cascade RCNN: High Quality Object Detection in Point CloudsCode1
Robust Collaborative 3D Object Detection in Presence of Pose ErrorsCode1
Cross-Modality Knowledge Distillation Network for Monocular 3D Object DetectionCode1
Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud LearningCode1
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel LogisticsCode1
Bridging the Domain Gap for Multi-Agent PerceptionCode1
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point CloudsCode1
PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object DetectionCode1
D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud SequenceCode1
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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