3 citations · 5 across the 8 of their papers we have counts for
10 papers
How Far Can 5,500 Hours of Driving Take You? A Scaling Law Analysis of Video Diffusion Models
Victor Besnier, Anh-Quan Cao, Elias Ramzi +5
Video generation for autonomous driving cannot follow the web-scale route: driving data is expensive to collect, bound by privacy requirements, and cannot be scraped at will, so mo…
OccAny: Generalized Unconstrained Urban 3D Occupancy
Anh-Quan Cao, Tuan-Hung Vu
Relying on in-domain annotations and precise sensor-rig priors, existing 3D occupancy prediction methods are limited in both scalability and out-of-domain generalization. While rec…
Driving on Registers
Ellington Kirby, Alexandre Boulch, Yihong Xu +11
We present DrivoR, a simple and efficient transformer-based architecture for end-to-end autonomous driving. Our approach builds on pretrained Vision Transformers (ViTs) and introdu…
StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic Datasets
Anh-Quan Cao, Ivan Lopes, Raoul de Charette
Multi-task learning for dense prediction is limited by the need for extensive annotation for every task, though recent works have explored training with partial task labels. Levera…
LatteCLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts
Anh-Quan Cao, Maximilian Jaritz, Matthieu Guillaumin +2
Large-scale vision-language pre-trained (VLP) models (e.g., CLIP) are renowned for their versatility, as they can be applied to diverse applications in a zero-shot setup. However,…
PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness
Anh-Quan Cao, Angela Dai, Raoul de Charette
We propose the task of Panoptic Scene Completion (PSC) which extends the recently popular Semantic Scene Completion (SSC) task with instance-level information to produce a richer u…
SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields
Anh-Quan Cao, Raoul de Charette
3D reconstruction from a single 2D image was extensively covered in the literature but relies on depth supervision at training time, which limits its applicability. To relax the de…
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation
Rong Li, Anh-Quan Cao, Raoul de Charette
Annotation of large-scale 3D data is notoriously cumbersome and costly. As an alternative, weakly-supervised learning alleviates such a need by reducing the annotation by several o…
MonoScene: Monocular 3D Semantic Scene Completion
Anh-Quan Cao, Raoul de Charette
MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense geometry and semantics of a scene are inferred from a single monocular RGB image. Different from…
PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds
Anh-Quan Cao, Gilles Puy, Alexandre Boulch +1
Rigid registration of point clouds with partial overlaps is a longstanding problem usually solved in two steps: (a) finding correspondences between the point clouds; (b) filtering…