Browse State-of-the-Art › 3D Object Detection
3D Object Detection
764 papers with code · 67 benchmarks · 67 datasets archive 2025-07-28
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 )
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
67 leaderboard tables shown for this task, 67 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 67 until expanded.
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
67 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 67 until expanded.
Subtasks archive 2025-07-28
5 subtasks in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
30 shown of 764 papers with code (1,576 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
-
25 Dec 2016 231 repositories listed Syntology ran 16 of 60 samples · 44 unverified · 22 pointer-only (licence)On the 156 classes not in COCO, YOLO9000 gets 16.
-
16 Apr 2019 76 repositories listed Syntology ran 10 of 130 samples · 120 unverifiedWe model an object as a single point --- the center point of its bounding box.
-
22 Nov 2017 68 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 2 pointer-only (licence)In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.
-
17 Nov 2017 44 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
-
26 Apr 2022 19 repositories listed Syntology ran 1 of 7 samples · 6 unverified · 1 pointer-only (licence)Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems.
-
14 Dec 2018 18 repositories listed Syntology ran 2 of 15 samples · 13 unverified · 1 pointer-only (licence)These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.
-
26 Mar 2019 16 repositories listed Syntology ran 1 of 17 samples · 16 unverifiedMost autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.
-
19 Jun 2020 13 repositories listed Syntology ran 7 of 22 samples · 15 unverifiedThree-dimensional objects are commonly represented as 3D boxes in a point-cloud.
-
21 Apr 2019 13 repositories listed Syntology ran 2 of 10 samples · 8 unverified · 10 pointer-only (licence)Current 3D object detection methods are heavily influenced by 2D detectors.
-
11 Dec 2018 13 repositories listed Syntology ran 5 of 9 samples · 4 unverified · 4 pointer-only (licence)In this paper, we propose PointRCNN for 3D object detection from raw point cloud.
-
31 Dec 2019 12 repositories listed Syntology ran 2 of 16 samples · 14 unverifiedWe present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds.
-
19 Apr 2022 11 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 1 pointer-only (licence)Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images.
-
1 Dec 2016 11 repositories listed Syntology ran 3 of 27 samples · 24 unverified · 5 pointer-only (licence)In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these…
-
16 Mar 2018 10 repositories listedWe introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only.
-
22 Apr 2021 9 repositories listed Syntology ran 7 of 22 samples · 15 unverifiedIn this paper, we study this problem with a practice built on a fully convolutional single-stage detector and propose a general framework FCOS3D.
-
23 Dec 2020 9 repositories listedDue to the fact that multi-modality data augmentation must maintain consistency between point cloud and images, recent methods in this field typically use relatively insufficient data augmentation.
-
15 Jan 2019 8 repositories listed Syntology ran 1 of 19 samples · 18 unverifiedA key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources.
-
23 Jun 2020 7 repositories listed Syntology ran 1 of 4 samples · 3 unverifiedHigh-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving.
-
31 Jul 2020 6 repositories listed Syntology ran 0 of 3 samples · 3 unverifiedSelf-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely.
-
8 Jul 2019 6 repositories listed Syntology ran 1 of 8 samples · 7 unverified3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications.
-
31 Dec 2020 5 repositories listedIn this paper, we take a slightly different viewpoint -- we find that precise positioning of raw points is not essential for high performance 3D object detection and that the coarse voxel granularity can also offer…
-
1 Jun 2018 5 repositories listedObjects can provide long-range geometric and scale constraints to improve camera pose estimation and reduce monocular drift.
-
4 May 2023 4 repositories listed Syntology ran 2 of 9 samples · 7 unverifiedTo combat this issue, several works divide point clouds into non-overlapping windows and constrain attentions in each local window.
-
15 Jan 2023 4 repositories listedHowever, due to the sparse characteristics of point clouds, it is non-trivial to apply a standard transformer on sparse points.
-
20 Jul 2022 4 repositories listedTo enable efficient long-range LiDAR-based object detection, we build a fully sparse 3D object detector (FSD).
-
3 Apr 2022 4 repositories listedIn addition, considering the property of sparse global distribution and density-varying local distribution of pedestrians, we further propose a novel method, Density-aware Hierarchical heatmap Aggregation (DHA), to…
-
1 Apr 2021 4 repositories listed Syntology ran 9 of 17 samples · 8 unverifiedInstead of grouping local points to each object candidate, our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers…
-
22 Jun 2020 4 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)3D object detection has been widely studied due to its potential applicability to many promising areas such as robotics and augmented reality.
-
22 Nov 2019 4 repositories listed Syntology ran 0 of 5 samples · 5 unverifiedSurprisingly, lidar-only methods outperform fusion methods on the main benchmark datasets, suggesting a gap in the literature.
-
13 Jul 2019 4 repositories listed Syntology ran 1 of 21 samples · 20 unverifiedUnderstanding the world in 3D is a critical component of urban autonomous driving.
Syntology lines on 23 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections