1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and
Domain Adaptation
- URL: http://arxiv.org/abs/2006.15505v1
- Date: Sun, 28 Jun 2020 04:49:39 GMT
- Title: 1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and
Domain Adaptation
- Authors: Zhuangzhuang Ding, Yihan Hu, Runzhou Ge, Li Huang, Sijia Chen, Yu
Wang, Jie Liao
- Abstract summary: We propose a one-stage, anchor-free and NMS-free 3D point cloud object detector AFDet.
AFDet serves as a strong baseline in our winning solution.
We design stronger networks and enhance the point cloud data using densification and point painting.
- Score: 7.807118356899879
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this technical report, we introduce our winning solution "HorizonLiDAR3D"
for the 3D detection track and the domain adaptation track in Waymo Open
Dataset Challenge at CVPR 2020. Many existing 3D object detectors include
prior-based anchor box design to account for different scales and aspect ratios
and classes of objects, which limits its capability of generalization to a
different dataset or domain and requires post-processing (e.g. Non-Maximum
Suppression (NMS)). We proposed a one-stage, anchor-free and NMS-free 3D point
cloud object detector AFDet, using object key-points to encode the 3D
attributes, and to learn an end-to-end point cloud object detection without the
need of hand-engineering or learning the anchors. AFDet serves as a strong
baseline in our winning solution and significant improvements are made over
this baseline during the challenges. Specifically, we design stronger networks
and enhance the point cloud data using densification and point painting. To
leverage camera information, we append/paint additional attributes to each
point by projecting them to camera space and gathering image-based perception
information. The final detection performance also benefits from model ensemble
and Test-Time Augmentation (TTA) in both the 3D detection track and the domain
adaptation track. Our solution achieves the 1st place with 77.11% mAPH/L2 and
69.49% mAPH/L2 respectively on the 3D detection track and the domain adaptation
track.
Related papers
- STONE: A Submodular Optimization Framework for Active 3D Object Detection [20.54906045954377]
Key requirement for training an accurate 3D object detector is the availability of a large amount of LiDAR-based point cloud data.
This paper proposes a unified active 3D object detection framework, for greatly reducing the labeling cost of training 3D object detectors.
arXiv Detail & Related papers (2024-10-04T20:45:33Z) - 3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features [70.50665869806188]
3DiffTection is a state-of-the-art method for 3D object detection from single images.
We fine-tune a diffusion model to perform novel view synthesis conditioned on a single image.
We further train the model on target data with detection supervision.
arXiv Detail & Related papers (2023-11-07T23:46:41Z) - Surface-biased Multi-Level Context 3D Object Detection [1.9723551683930771]
This work addresses the object detection task in 3D point clouds using a highly efficient, surface-biased, feature extraction method (wang2022rbgnet)
We propose a 3D object detector that extracts accurate feature representations of object candidates and leverages self-attention on point patches, object candidates, and on the global scene in 3D scene.
arXiv Detail & Related papers (2023-02-13T11:50:04Z) - SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from
Point Cloud [125.9472454212909]
We present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D)
SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage.
Experiments show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label.
arXiv Detail & Related papers (2022-12-06T09:32:44Z) - AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
We propose a novel 3D detection framework that associates intact features for objects via domain adaptation.
We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
arXiv Detail & Related papers (2022-08-24T16:54:38Z) - SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object
Detection [78.90102636266276]
We propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA)
Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling.
In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection.
arXiv Detail & Related papers (2022-01-06T08:54:47Z) - 3D Object Detection Combining Semantic and Geometric Features from Point
Clouds [19.127930862527666]
We propose a novel end-to-end two-stage 3D object detector named SGNet for point clouds scenes.
The VTPM is a Voxel-Point-Based Module that finally implements 3D object detection in point space.
As of September 19, 2021, for KITTI dataset, SGNet ranked 1st in 3D and BEV detection on cyclists with easy difficulty level, and 2nd in the 3D detection of moderate cyclists.
arXiv Detail & Related papers (2021-10-10T04:43:27Z) - ST3D: Self-training for Unsupervised Domain Adaptation on 3D
ObjectDetection [78.71826145162092]
We present a new domain adaptive self-training pipeline, named ST3D, for unsupervised domain adaptation on 3D object detection from point clouds.
Our ST3D achieves state-of-the-art performance on all evaluated datasets and even surpasses fully supervised results on KITTI 3D object detection benchmark.
arXiv Detail & Related papers (2021-03-09T10:51:24Z) - RoIFusion: 3D Object Detection from LiDAR and Vision [7.878027048763662]
We propose a novel fusion algorithm by projecting a set of 3D Region of Interests (RoIs) from the point clouds to the 2D RoIs of the corresponding the images.
Our approach achieves state-of-the-art performance on the KITTI 3D object detection challenging benchmark.
arXiv Detail & Related papers (2020-09-09T20:23:27Z) - Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image.
Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space.
We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step.
This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
arXiv Detail & Related papers (2020-08-31T17:10:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.