17 citations · 17 across the 9 of their papers we have counts for
11 papers · 1 filter
S2Tok: Streaming 3D Gaussian Reconstruction with Persistent Spatial Tokens
Fang Li, Jiraphon Yenphraphai, Quentin Herau +5
Streaming 3D reconstruction requires more than a sequence of geometric predictions: it requires a persistent scene state that can incorporate new evidence and remain renderable as…
Building Rome from a Single Image
Jiraphon Yenphraphai, Fang Li, Tianshuo Xu +5
Single-image scene generation aims to produce a complete 3D scene mesh from a single image, including surfaces the camera did not observe. While pretrained 3D object generators enc…
Teaching Video Generators to Remember: Eliciting Dynamic Memory for Out-of-Sight State Evolution
Tianshuo Xu, Yichen Xie, Depu Meng +5
Video world models should maintain evolving states when evidence is unobserved, yet current generators often freeze hidden states upon interruption. This is not simply a capacity p…
SpectralSplat: Appearance-Disentangled Feed-Forward Gaussian Splatting for Driving Scenes
Quentin Herau, Tianshuo Xu, Depu Meng +5
Feed-forward 3D Gaussian Splatting methods have achieved impressive reconstruction quality for autonomous driving scenes, yet they entangle scene geometry with transient appearance…
URoPE: Universal Relative Position Embedding across Geometric Spaces
Yichen Xie, Depu Meng, Chensheng Peng +4
Relative position embedding has become a standard mechanism for encoding positional information in Transformers. However, existing formulations are typically limited to a fixed geo…
Learning to Drive is a Free Gift: Large-Scale Label-Free Autonomy Pretraining from Unposed In-The-Wild Videos
Matthew Strong, Wei-Jer Chang, Quentin Herau +4
Ego-centric driving videos available online provide an abundant source of visual data for autonomous driving, yet their lack of annotations makes it difficult to learn representati…
UniQueR: Unified Query-based Feedforward 3D Reconstruction
Chensheng Peng, Quentin Herau, Jiezhi Yang +6
We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images. Existing feedforward models such as DUSt3R, VGGT,…
Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering
Quentin Herau, Nathan Piasco, Moussab Bennehar +6
Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccuracies in sensor calibration and e…
3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration
Quentin Herau, Moussab Bennehar, Arthur Moreau +6
Reliable multimodal sensor fusion algorithms require accurate spatiotemporal calibration. Recently, targetless calibration techniques based on implicit neural representations have…
SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields
Quentin Herau, Nathan Piasco, Moussab Bennehar +5
In rapidly-evolving domains such as autonomous driving, the use of multiple sensors with different modalities is crucial to ensure high operational precision and stability. To corr…
MOISST: Multimodal Optimization of Implicit Scene for SpatioTemporal calibration
Quentin Herau, Nathan Piasco, Moussab Bennehar +5
With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise. However, in order to make use of the informat…