DeepAI AI Chat
Log In Sign Up

SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation

08/15/2021
by   Qiangeng Xu, et al.
University of Southern California
0

In autonomous driving, a LiDAR-based object detector should perform reliably at different geographic locations and under various weather conditions. While recent 3D detection research focuses on improving performance within a single domain, our study reveals that the performance of modern detectors can drop drastically cross-domain. In this paper, we investigate unsupervised domain adaptation (UDA) for LiDAR-based 3D object detection. On the Waymo Domain Adaptation dataset, we identify the deteriorating point cloud quality as the root cause of the performance drop. To address this issue, we present Semantic Point Generation (SPG), a general approach to enhance the reliability of LiDAR detectors against domain shifts. Specifically, SPG generates semantic points at the predicted foreground regions and faithfully recovers missing parts of the foreground objects, which are caused by phenomena such as occlusions, low reflectance or weather interference. By merging the semantic points with the original points, we obtain an augmented point cloud, which can be directly consumed by modern LiDAR-based detectors. To validate the wide applicability of SPG, we experiment with two representative detectors, PointPillars and PV-RCNN. On the UDA task, SPG significantly improves both detectors across all object categories of interest and at all difficulty levels. SPG can also benefit object detection in the original domain. On the Waymo Open Dataset and KITTI, SPG improves 3D detection results of these two methods across all categories. Combined with PV-RCNN, SPG achieves state-of-the-art 3D detection results on KITTI.

READ FULL TEXT

page 2

page 19

12/06/2022

SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud

LiDAR-based 3D object detection is an indispensable task in advanced aut...
10/16/2020

SF-UDA^3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection

3D object detectors based only on LiDAR point clouds hold the state-of-t...
03/13/2023

Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection

Current 3D object detection models follow a single dataset-specific trai...
10/18/2021

FAST3D: Flow-Aware Self-Training for 3D Object Detectors

In the field of autonomous driving, self-training is widely applied to m...
12/06/2018

OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation

Object detectors tend to perform poorly in new or open domains, and requ...
08/27/2020

GhostBuster: Looking Into Shadows to Detect Ghost Objects in Autonomous Vehicle 3D Sensing

LiDAR-driven 3D sensing allows new generations of vehicles to achieve ad...
06/28/2020

1st Place Solution for Waymo Open Dataset Challenge – 3D Detection and Domain Adaptation

In this technical report, we introduce our winning solution "HorizonLiDA...