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
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:2506. 22784v2 Announce Type: replace-cross Abstract: Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception.
By Yu Han, Zhiwei Huang, Yanting Zhang, Fangjun Ding, Shen Cai, Xiaoyu Tang, Yanchao Dong, Rui Fan
arXiv:2601. 20720v2 Announce Type: replace-cross Abstract: End-to-end perception and trajectory prediction from raw sensor data is one of the key capabilities for autonomous driving.
By Matej Halinkovic, Nina Masarykova, Alexey Vinel, Marek Galinski
arXiv:2602. 23172v2 Announce Type: replace-cross Abstract: Capturing 4D spatiotemporal scene structure is crucial for the safe and reliable operation of robots in dynamic environments.
By Maximilian Luz, Rohit Mohan, Thomas N\"urnberg, Yakov Miron, Daniele Cattaneo, Abhinav Valada
arXiv:2608. 07579v1 Announce Type: cross Abstract: The AI City Challenge 2026 Track 1 evaluates multi-camera 3D perception in large indoor warehouses under a synthetic-to-real (Sim2Real) setting; depth is available only for training and validation, so inference is RGB-only.
By Abdullah Naeem, Anav Katwal, Ayon Dey, Noman Khan, Md Tamjidul Hoque
arXiv:2606. 26700v1 Announce Type: cross Abstract: Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation.
By Sajid Ansari, Arthi, Girish Varma, Antony Thomas
arXiv:2606. 10019v1 Announce Type: cross Abstract: We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings.
By Ray Zhang, Marcus Greiff, Thomas Lew, John Subosits
arXiv:2608. 19536v1 Announce Type: cross Abstract: Learning-based global point cloud registration has achieved remarkable progress, yet its reliance on geometric representations makes existing methods sensitive to variations in point density, scan pattern, viewpoint, and sensor characteristics.
By Eunsoo Im, Junghun Suh, Gyeonggwan Lee, Seunghwan Hong
arXiv:2606. 07233v1 Announce Type: cross Abstract: LiDAR-based 3D Multi-Object Tracking (MOT) typically relies solely on geometric information, which is often insufficient to distinguish between targets during prolonged occlusions or in crowded human-populated environments.
By Eduardo Borges, Lu\'is Garrote, Urbano J. Nunes
GS‑Net is a lightweight plug‑and‑play module that expands sparse Structure‑from‑Motion point clouds into dense Gaussian primitives, enabling cross‑sensor view synthesis for autonomous driving. It learns a generalizable initialization for 3D Gaussian Splatting, improving rendering quality for both interpolated and extrapolated camera viewpoints. The authors introduce CARLA‑NVS, a benchmark with 12 uniformly spaced cameras, and show that GS‑Net outperforms standard 3DGS by 2.08 dB PSNR on interpolated views and 1.86 dB on extrapolated views while being 50× faster to initialize.
By Yichen Zhang, Zihan Wang, Jiali Han, Peilin Li, Jiaxun Zhang, Jianqiang Wang, Lei He, Keqiang Li
arXiv:2608.22398v1 Announce Type: cross
Abstract: Reliably tracking moving deformable linear objects (DLOs) while simultaneously ensuring robustness, accuracy, and temporally consistent state estimat...
By Annalena Hartmann, Priyamvada Ajithkumar, Patrick Br\"undl, J\"org Franke