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 176–200 of 1576 papers

TitleStatusHype
ROA-BEV: 2D Region-Oriented Attention for BEV-based 3D Object—0
HeightFormer: A Semantic Alignment Monocular 3D Object Detection Method from Roadside Perspective—0
Progressive Multi-Modal Fusion for Robust 3D Object Detection—0
QuadBEV: An Efficient Quadruple-Task Perception Framework via Bird's-Eye-View Representation—0
STONE: A Submodular Optimization Framework for Active 3D Object DetectionCode0
Learning 3D Perception from Others' Predictions—0
Finetuning Pre-trained Model with Limited Data for LiDAR-based 3D Object Detection by Bridging Domain Gaps—0
Gaussian-Det: Learning Closed-Surface Gaussians for 3D Object Detection—0
Panopticus: Omnidirectional 3D Object Detection on Resource-constrained Edge Devices—0
3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for 3D Object DetectionCode2
Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection—0
DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy PredictionCode2
VLMine: Long-Tail Data Mining with Vision Language Models—0
UniBEVFusion: Unified Radar-Vision BEVFusion for 3D Object Detection—0
Sparse-to-Dense LiDAR Point Generation by LiDAR-Camera Fusion for 3D Object Detection—0
RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking FrameworkCode2
UltimateDO: An Efficient Framework to Marry Occupancy Prediction with 3D Object Detection via Channel2heightCode4
TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection—0
Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation—0
Directed-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attention—0
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point CloudsCode0
Semi-Supervised 3D Object Detection with Channel Augmentation using Transformation Equivariance—0
LEROjD: Lidar Extended Radar-Only Object DetectionCode1
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object DetectionCode0
Multi-V2X: A Large Scale Multi-modal Multi-penetration-rate Dataset for Cooperative PerceptionCode0
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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