6 papers
First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstruction
Remi Chierchia, Léo Lebrat, David Ahmedt-Aristizabal +3
Neural Surface Reconstruction has become a standard methodology for indoor 3D reconstruction, with Signed Distance Functions (SDFs) proving particularly effective for representing…
In Depth We Trust: Reliable Monocular Depth Supervision for Gaussian Splatting
Wenhui Xiao, Ethan Goan, Rodrigo Santa Cruz +4
Using accurate depth priors in 3D Gaussian Splatting helps mitigate artifacts caused by sparse training data and textureless surfaces. However, acquiring accurate depth maps requir…
DCHM: Depth-Consistent Human Modeling for Multiview Detection
Jiahao Ma, Tianyu Wang, Miaomiao Liu +2
Multiview pedestrian detection typically involves two stages: human modeling and pedestrian localization. Human modeling represents pedestrians in 3D space by fusing multiview info…
Puzzles: Unbounded Video-Depth Augmentation for Scalable End-to-End 3D Reconstruction
Jiahao Ma, Lei Wang, Miaomiao liu +2
Multi-view 3D reconstruction remains a core challenge in computer vision. Recent methods, such as DUST3R and its successors, directly regress pointmaps from image pairs without rel…
SOAF: Scene Occlusion-aware Neural Acoustic Field
Huiyu Gao, Jiahao Ma, David Ahmedt-Aristizabal +2
This paper tackles the problem of novel view audio-visual synthesis along an arbitrary trajectory in an indoor scene, given the audio-video recordings from other known trajectories…
HashPoint: Accelerated Point Searching and Sampling for Neural Rendering
Jiahao Ma, Miaomiao Liu, David Ahmedt-Aristizaba +1
In this paper, we address the problem of efficient point searching and sampling for volume neural rendering. Within this realm, two typical approaches are employed: rasterization a…