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 276–300 of 1576 papers

TitleStatusHype
BiCo-Fusion: Bidirectional Complementary LiDAR-Camera Fusion for Semantic- and Spatial-Aware 3D Object Detection—0
STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning—0
CTS: Sim-to-Real Unsupervised Domain Adaptation on 3D Detection—0
Towards Open-set Camera 3D Object Detection—0
MDHA: Multi-Scale Deformable Transformer with Hybrid Anchors for Multi-View 3D Object DetectionCode0
MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object DetectionCode1
DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object DetectionCode0
Multi-Dimensional Pruning: Joint Channel, Layer and Block Pruning with Latency Constraint—0
Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object DetectionCode0
Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding—0
Syn-to-Real Unsupervised Domain Adaptation for Indoor 3D Object DetectionCode0
SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detection—0
Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object DetectionCode2
EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation ModelsCode2
Shelf-Supervised Cross-Modal Pre-Training for 3D Object DetectionCode0
BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in Vision-based Roadside 3D Object DetectionCode2
CT3D++: Improving 3D Object Detection with Keypoint-induced Channel-wise TransformerCode0
Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D SensingCode0
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy NetworkCode2
RS-DFM: A Remote Sensing Distributed Foundation Model for Diverse Downstream Tasks—0
UCDNet: Multi-UAV Collaborative 3D Object Detection Network by Reliable Feature Mapping—0
UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection—0
Multi-Object Tracking based on Imaging Radar 3D Object Detection—0
GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision TransformerCode2
Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object DetectionCode3
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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