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 1–25 of 1576 papers

TitleStatusHype
Dual LiDAR-Based Traffic Movement Count Estimation at a Signalized Intersection: Deployment, Data Collection, and Preliminary Analysis—0
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR RepresentationsCode1
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object DetectionCode2
A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects—0
Vision-based Lifting of 2D Object Detections for Automated Driving—0
Teleoperated Driving: a New Challenge for 3D Object Detection in Compressed Point Clouds—0
DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos—0
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting—0
Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation ModelsCode3
SpatialLM: Training Large Language Models for Structured Indoor Modeling—0
SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding—0
FALO: Fast and Accurate LiDAR 3D Object Detection on Resource-Constrained Devices—0
Simulate Any Radar: Attribute-Controllable Radar Simulation via Waveform Parameter EmbeddingCode2
Rooms from Motion: Un-posed Indoor 3D Object Detection as Localization and Mapping—0
The Meeseeks Mesh: Spatially Consistent 3D Adversarial Objects for BEV Detector—0
RQR3D: Reparametrizing the regression targets for BEV-based 3D object detection—0
CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving—0
InstanceBEV: Unifying Instance and BEV Representation for Global Modeling—0
MoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection—0
DepthFusion: Depth-Aware Hybrid Feature Fusion for LiDAR-Camera 3D Object Detection—0
PillarMamba: Learning Local-Global Context for Roadside Point Cloud via Hybrid State Space Model—0
MonoCoP: Chain-of-Prediction for Monocular 3D Object Detection—0
DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic FusionCode1
Efficient On-Chip Implementation of 4D Radar-Based 3D Object Detection on Hailo-8L—0
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MegFusionNDS0.77—Unverified
3MMFusion-eNDS0.77—Unverified
4DeepInteraction-largeNDS0.76—Unverified
5DeepInteraction-eNDS0.76—Unverified
6BEVFusion-eNDS0.76—Unverified
7RacoonPowerNDS0.76—Unverified
8ADS-TEAMNDS0.75—Unverified
9CenterPoint-FusionNDS0.75—Unverified
10UniTRNDS0.75—Unverified