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 976–1000 of 1576 papers

TitleStatusHype
Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception—0
Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data—0
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images—0
Delving into the Pre-training Paradigm of Monocular 3D Object Detection—0
Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?—0
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network—0
Dense Voxel Fusion for 3D Object Detection—0
Density-Based Clustering for 3D Object Detection in Point Clouds—0
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization—0
Depth-aware Fusion Method based on Image and 4D Radar Spectrum for 3D Object Detection—0
Depth-discriminative Metric Learning for Monocular 3D Object Detection—0
DepthFusion: Depth-Aware Hybrid Feature Fusion for LiDAR-Camera 3D Object Detection—0
DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention—0
DetVPCC: RoI-based Point Cloud Sequence Compression for 3D Object Detection—0
Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection—0
DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection—0
DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation—0
Diffusion-based 3D Object Detection with Random Boxes—0
Digging Into Output Representation for Monocular 3D Object Detection—0
Dimension Embeddings for Monocular 3D Object Detection—0
Directed-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attention—0
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation—0
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment—0
DisARM: Displacement Aware Relation Module for 3D Detection—0
Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MegFusionNDS0.77—Unverified
3MMFusion-eNDS0.77—Unverified
4BEVFusion-eNDS0.76—Unverified
5RacoonPowerNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8FusionVPENDS0.75—Unverified
9FocalFormer3D-FNDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified