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 1226–1250 of 1576 papers

TitleStatusHype
Geometry-based Distance Decomposition for Monocular 3D Object DetectionCode1
Multimodal Object Detection via Probabilistic EnsemblingCode1
Objects are Different: Flexible Monocular 3D Object DetectionCode1
HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object DetectionCode1
Group-Free 3D Object Detection via TransformersCode1
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object DetectionCode1
3D-MAN: 3D Multi-frame Attention Network for Object Detection—0
Delving into Localization Errors for Monocular 3D Object DetectionCode1
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object DetectionCode1
Fooling LiDAR Perception via Adversarial Trajectory PerturbationCode1
SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point CloudCode1
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection—0
Resonant Scanning Design and Control for Fast Spatial SamplingCode0
M3DSSD: Monocular 3D Single Stage Object DetectorCode1
X-view: Non-egocentric Multi-View 3D Object Detector—0
Stereo Object Matching Network—0
MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty PropagationCode1
TICaM: A Time-of-flight In-car Cabin Monitoring Dataset—0
Stereo CenterNet based 3D Object Detection for Autonomous Driving—0
Suppress-and-Refine Framework for End-to-End 3D Object DetectionCode1
RangeDet:In Defense of Range View for LiDAR-based 3D Object DetectionCode1
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D DetectionCode1
3D-FFS: Faster 3D object detection with Focused Frustum Search in sensor fusion based networks—0
S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object DetectionCode1
Holistic 3D Scene Understanding from a Single Image with Implicit RepresentationCode1
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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