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 1426–1450 of 1576 papers

TitleStatusHype
Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted DataCode0
DenseFusion: 6D Object Pose Estimation by Iterative Dense FusionCode0
GraVoS: Voxel Selection for 3D Point-Cloud DetectionCode0
BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using DepthCode0
Warp and Learn: Novel Views Generation for Vehicles and Other ObjectsCode0
AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene UnderstandingCode0
DeepCompress: Efficient Point Cloud Geometry CompressionCode0
QUEST: Query Stream for Practical Cooperative PerceptionCode0
Semi-supervised 3D Object Detection with PatchTeacher and PillarMixCode0
Gradient-based Maximally Interfered Retrieval for Domain Incremental 3D Object DetectionCode0
Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object DetectionCode0
Towards Fair and Comprehensive Comparisons for Image-Based 3D Object DetectionCode0
Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D SensingCode0
FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object DetectionCode0
Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object DetectionCode0
Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVsCode0
SESS: Self-Ensembling Semi-Supervised 3D Object DetectionCode0
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous DrivingCode0
Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object DetectionCode0
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance SegmentationCode0
Shape2Scene: 3D Scene Representation Learning Through Pre-training on Shape DataCode0
Shape Anchor Guided Holistic Indoor Scene UnderstandingCode0
Three-dimensional Backbone Network for 3D Object Detection in Traffic ScenesCode0
CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box PredictionCode0
Shelf-Supervised Cross-Modal Pre-Training for 3D Object DetectionCode0
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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