Synthesizing a novel-view video from a monocular reference video along a target camera trajectory requires both geometric consistency and motion fidelity with respect to the reference video. Existing methods based on explicit 3D representations are limited by the accuracy of off-the-shelf reconstruction modules, which often produce inaccurate geometry for dynamic objects in monocular videos.
PIVOT is a new multi‑trajectory dataset and evaluation framework that captures real‑world scenes with diverse camera paths, preserving both sensor‑derived measured poses and COLMAP‑optimized poses along with calibrated and optimized intrinsics. It defines three benchmark families—seen vs. unseen trajectory generalization, measured vs. optimized pose sensitivity, and calibrated vs. optimized intrinsics sensitivity—and introduces a directed pose‑space Chamfer distance to assess pose coverage. The first version of PIVOT includes five scenes recorded with a DJI Mini 4 Pro and offers an open processing and Nerfstudio‑based evaluation toolchain, revealing a consistent quality gap between held‑out and unseen trajectories and significant sensitivity to pose source and camera intrinsics.
By Mary Raymond
arXiv:2610.01314v1 Announce Type: new
Abstract: Dynamic scenes may be captured by a moving camera, multiple video streams, or images taken at different times. These observations reveal complementary...
By Ilya Fradlin, Christian Schmidt, Jens Piekenbrinck, Karim Knaebel, Gonzalo Martin Garcia, Bastian Leibe
TrackEverything is a 3D point tracker that overcomes the trade‑off between sparse long‑horizon tracking and dense short‑clip tracking by representing videos as persistent 3D scene tracks in world coordinates. It introduces voxel‑based de‑duplication at sliding‑window boundaries, a two‑stage refinement process (endpoint refiner and lightweight trajectory refiner), and a 3D WAFT module that replaces memory‑heavy 4D correlation volumes with efficient feature sampling. The method can track all visible points in videos longer than 1000 frames using only 40 GB of GPU memory, outperforming existing dense trackers on short clips and matching sparse trackers on long sequences.
By Ayush Jain, Sreeharsha Paruchuri, Ishita Gupta, Fan Zhang, Tanner Schmidt, Jakob Engel, Katerina Fragkiadaki, Adam W. Harley
Point2Pose is a model‑free method for causal 6D pose tracking of multiple rigid objects using monocular RGB‑D video. It starts from sparse image points and employs a 2D point tracker to maintain long‑range correspondences, allowing instant recovery after complete occlusion. The system also incrementally builds an online Truncated Signed Distance Function (TSDF) representation of the tracked objects and introduces a new multi‑object tracking dataset with motion‑capture ground truth.
By Tzu-Yuan Lin, Ho Jae Lee, Kevin Doherty, Yonghyeon Lee, Sangbae Kim
arXiv:2603.12064v3 Announce Type: replace
Abstract: We address the challenging problem of dense dynamic scene reconstruction and camera pose estimation from multiple freely moving cameras -- a settin...
By Shuo Sun, Unal Artan, Malcolm Mielle, Achim J. Lilienthaland, Martin Magnusson
arXiv:2608.13147v2 Announce Type: replace
Abstract: Camera-based autonomous driving perception requires a shared representation that preserves metric 3D structure across synchronized multi-camera str...
By Longfei Xu, Xiaohui Wang, Zehao Huang, Han Li, Ya Yang, Naiyan Wang, Si Liu
arXiv:2608.22039v1 Announce Type: new
Abstract: Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging came...
By Sara Sabour, Linyi Jin, Richard Tucker, Amir Hertz, Marcus Brubaker, Saurabh Saxena, Junhwa Hur, Andrea Tagliasacchi, Deqing Sun, David J. Fleet, Richard Szeliski, Noah Snavely
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
The paper introduces PointVGGT, a zero‑shot framework for multiview RGB‑D point cloud registration that replaces the traditional pairwise‑then‑global pipeline. It employs a foundation‑then‑refinement paradigm, using visual geometry foundation models to directly recover metrically consistent global poses and then refining them with voxelized spatial hashing and IRLS‑based bundle adjustment. Experiments on indoor, object‑centric, and outdoor datasets demonstrate superior registration accuracy and computational efficiency without any training.
By Haobo Jiang, Liang Yu, Jianmin Zheng
arXiv:2603.14528v3 Announce Type: replace
Abstract: In recent years, 3D visual foundation models, pioneered by pointmap-based approaches such as DUSt3R, have attracted a lot of interest, achieving im...
By Shuang Guo, Filbert Febryanto, Lei Sun, Luc Van Gool, Guillermo Gallego
The paper introduces a reliability-regulated trajectory optimization framework for progressive COLMAP‑free 3D Gaussian Splatting (3DGS). It uses a self‑supervised bidirectional cycle‑consistency mechanism to control camera trajectory estimation through forward motion propagation and retrospective trajectory correction, thereby reducing error compounding without external priors. Experiments on Tanks and Temples and CO3D‑V2 demonstrate improved camera trajectory accuracy and novel‑view rendering quality compared to existing unposed baselines.
By Zijian Wu, Jinliang Wang, Zidian Lin, Ying Song, Ziqian Lu, Hanjie Ma, Zhen Ye, Mingfeng Jiang