Global 6DOF Pose Estimation from Untextured 2D City Models

BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the object. Our method works for arbitrary rigid objects, even when visual texture is largely absent. The object is assumed to be segmented in the first frame only. No additional information is required, and no assumption is made about the interaction agent. Key to our method is a Neural Object Field that is learned concurrently with a pose graph optimization process in order to robustly accumulate information into a consis…

Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection

Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving. Recently, methods for 3D vehicle detection solely based on monocular RGB images gained popularity. In order to facilitate this task as well as to compare and drive state-of-the-art methods, several new datasets and benchmarks have been published. Ground truth annotations of vehicles are usually obtained using lidar point clouds, which often induces errors due to imperfect calibration or synchronization between both sensors. To this end, we propose Citysc…

城市尺度下第一人称视觉惯性SLAM的基准测试

Benchmarking Egocentric Visual-Inertial SLAM at City Scale

针对现有SLAM基准无法匹配可穿戴设备第一人称数据的挑战、缺乏高精度真值的问题，本文提出城市尺度的第一人称多模态视觉惯性SLAM数据集与基准，含厘米级精度位姿标注，验证了现有SOTA系统鲁棒性不足，并设计多难度赛道助力方法评估。

Geometric Loss Functions for Camera Pose Regression with Deep Learning

Deep learning has shown to be effective for robust and real-time monocular image relocalisation. In particular, PoseNet is a deep convolutional neural network which learns to regress the 6-DOF camera pose from a single image. It learns to localize using high level features and is robust to difficult lighting, motion blur and unknown camera intrinsics, where point based SIFT registration fails. However, it was trained using a naive loss function, with hyper-parameters which require expensive tuning. In this paper, we give the problem a more fundamental theoretical treatment. We explore a number…

ToosiCubix：基于车辆部件标注的单目 3D 长方体标注

ToosiCubix: Monocular 3D Cuboid Labeling via Vehicle Part Annotations

ToosiCubix 是一种仅需单目图像和相机内参、通过约 10 次点击标注车辆部件以估计 3D 长方体的方法，为现有数据集提供了低成本、可扩展的高质量标注方案。

基于冗余切片观测与A-Contrario验证的跨视角定位

Cross-View Localization via Redundant Sliced Observations and A-Contrario Validation

针对跨视角定位缺乏冗余观测的问题，提出Slice-Loc方法，通过分割图像生成冗余位姿观测，结合A-Contrario验证滤除错误，显著提升定位精度并有效检测失败。