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 651–675 of 1576 papers

TitleStatusHype
Learning Occupancy for Monocular 3D Object DetectionCode1
RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion—0
Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving—0
DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object Detection—0
RCFusion: Fusing 4-D Radar and Camera With Bird’s-Eye View Features for 3-D Object Detection—0
Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection—0
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer—0
MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes—0
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation ModelsCode1
Multi-Modal 3D Object Detection by Box MatchingCode1
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving—0
SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object DetectionCode1
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point CloudsCode2
3D Small Object Detection with Dynamic Spatial PruningCode1
Aligning Bird-Eye View Representation of Point Cloud Sequences using Scene FlowCode1
OctFormer: Octree-based Transformers for 3D Point CloudsCode2
TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene UnderstandingCode2
TransCAR: Transformer-based Camera-And-Radar Fusion for 3D Object Detection—0
InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors—0
Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object DetectionCode1
HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score FilteringCode0
Gradient-based Maximally Interfered Retrieval for Domain Incremental 3D Object DetectionCode0
OriCon3D: Effective 3D Object Detection using Orientation and Confidence—0
SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object DetectionCode2
Group Equivariant BEV for 3D Object Detection—0
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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