Con-DSO introduces a consistency-aware RGB‑D direct sparse odometry framework that learns pixel‑level photometric and geometric uncertainty from adjacent RGB‑D frame pairs. The network predicts uncertainties that are converted into pairwise quality scores, guiding support‑pixel selection and forming a host‑side quality prior for keyframe tracking. Experiments on five public benchmarks show that this approach reduces absolute trajectory error by over 20% on ICL‑NUIM and by 50–80% on other datasets, improving robustness in challenging environments.
By Haolan Zhang, Thanh Nguyen Canh, Chenghao Li, Ziyan Gao, Xiongwen Jiang, Nak Young Chong
arXiv:2504.15776v2 Announce Type: replace
Abstract: Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccur...
By Quentin Herau, Nathan Piasco, Moussab Bennehar, Luis Rold\~ao, Dzmitry Tsishkou, Bingbing Liu, Cyrille Migniot, Pascal Vasseur, C\'edric Demonceaux
The paper introduces a benchmark for evaluating Structure-from-Motion (SfM) methods used in novel view synthesis (NVS) such as NeRFs and 3DGS. It compares classical SfM, which relies on local feature matches and bundle adjustment, with recent neural network approaches that regress camera parameters directly for faster but less accurate results. The study finds that reducing feature counts speeds up classical SfM without sacrificing pose accuracy, and that combining neural network initial estimates with classical refinement yields the best balance of efficiency and effectiveness.
By Jhacson Meza, Martin R. Oswald, Torsten Sattler
arXiv:2609.13733v1 Announce Type: new
Abstract: Stable and reliable 4D spatial understanding is fundamental for autonomous driving systems. While feedforward reconstruction networks can estimate came...
By Meng-Li Shih, Shih-Yang Su, Yuliang Zou, Hao Xiang, Haidong Zhu, Vincent Casser, Brian Curless, Dmitry Kalenichenko, Mingxing Tan, Dragomir Anguelov
TAPVid-MV is a new benchmark for tracking any point in 3D across multiple synchronized camera views. It comprises 284 sequences, 1,142 calibrated camera streams, and 109,769 point tracks, covering indoor and outdoor domains and derived from various modalities such as depth, LiDAR, SLAM, and simulation. The dataset is visually verified, and evaluation shows that current multi‑view trackers do not consistently outperform monocular trackers, highlighting geometry recovery as a key bottleneck.
By Skanda Koppula, Frano Rajic, Abdullah Faiz Ur Rahman, Yi Yang, Ignacio Rocco, Jeet Thakwani, Rishabh Kabra, Andrew Zisserman, Joao Carreira, Siyu Tang, Carl Doersch, Gabriel Brostow
arXiv:2609.40244v1 Announce Type: new
Abstract: Mobile robots and vehicles carry synchronized multi-camera rigs, yet many streaming 3D foundation models are designed for monocular input, leaving effi...
By Yufei Wei, Shuhao Ye, Qi Wang, Xin Zheng, Qing Huang, Rong Xiong, Yue Wang
arXiv:2509.04600v2 Announce Type: replace
Abstract: Reconstructing global human motion from monocular video is fundamental to VR, graphics, and robotics, yet remains ill-posed due to depth ambiguity,...
By Zhongyuan Hu, Qijun Ying, Jiazhi Shu, Ronghui Li, Yu Lu, Zijiao Zeng, Xiu Li
MDE-VIO integrates learned depth priors into the VINS-Mono optimization backend to improve visual‑inertial odometry in low‑texture environments. The framework enforces affine‑invariant depth consistency and pairwise ordinal constraints while filtering unstable artifacts with variance‑based gating, keeping computation within edge‑device limits. Experiments on TartanGround and M3ED datasets show the method prevents divergence and reduces Absolute Trajectory Error by up to 28.3%.
By Arda Alniak, Sinan Kalkan, Mustafa Mert Ankarali, Afsar Saranli, Abdullah Aydin Alatan
Mobile robots and vehicles carry synchronized multi-camera rigs, yet many streaming 3D foundation models are designed for monocular input, leaving efficient use of rig geometry a challenge. We present...
MIRAGE is a method that fuses feed‑forward visual geometry with LiDAR‑IMU metric motion to provide reliable metric pose estimation and high‑fidelity Gaussian reconstruction. It regulates pose corrections based on correspondence support and cross‑modal agreement, achieving accurate metric trajectories and outperforming COLMAP on datasets such as Oxford Spires, M2DGR, and INS. Additionally, MIRAGE was used to create a new multimodal data‑collection platform and establish accurate 6‑DoF reference trajectories for a vision‑only benchmark, enabling systematic evaluation of monocular large‑scale reconstruction methods in long‑horizon indoor scenes.
By Xinzhe Wang, Changjian Jiang, Kaiwen Song, Xudong Li, Kerui Ren, Ran Yi, Lizhuang Ma, Chunhua Shen, Linning Xu, Tao Lu, Mulin Yu
arXiv:2609.24482v1 Announce Type: new
Abstract: Monocular 3D human pose estimation (HPE) remains challenging due to depth ambiguity, occlu- sions, and the need for temporal consistency. While multi-v...
By Mena Kamel, Natalie Won, Amrut Sarangi, Sven Jager, Albert Pla Planas
arXiv:2608.24544v1 Announce Type: new
Abstract: Many feature-based visual-inertial odometry (VIO) systems rely on sparse feature tracking, whose accuracy and robustness directly affect state estimati...
By Renbiao Jin, Danping Zou, Wenxian Yu