53 citations · 87 across the 10 of their papers we have counts for
11 papers
Twinner: Shining Light on Digital Twins in a Few Snaps
Jesus Zarzar, Tom Monnier, Roman Shapovalov +2
We present the first large reconstruction model, Twinner, capable of recovering a scene's illumination as well as an object's geometry and material properties from only a few posed…
UnCommon Objects in 3D
Xingchen Liu, Piyush Tayal, Jianyuan Wang +10
We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-reso…
Deep Learning at the Intersection: Certified Robustness as a Tool for 3D Vision
Gabriel Pérez S, Juan C. Pérez, Motasem Alfarra +4
This paper presents preliminary work on a novel connection between certified robustness in machine learning and the modeling of 3D objects. We highlight an intriguing link between…
TrackNeRF: Bundle Adjusting NeRF from Sparse and Noisy Views via Feature Tracks
Jinjie Mai, Wenxuan Zhu, Sara Rojas +6
Neural radiance fields (NeRFs) generally require many images with accurate poses for accurate novel view synthesis, which does not reflect realistic setups where views can be spars…
SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation
Jesus Zarzar, Bernard Ghanem
We present a novel approach for digitizing real-world objects by estimating their geometry, material properties, and environmental lighting from a set of posed images with fixed li…
Enhancing Neural Rendering Methods with Image Augmentations
Juan C. Pérez, Sara Rojas, Jesus Zarzar +1
Faithfully reconstructing 3D geometry and generating novel views of scenes are critical tasks in 3D computer vision. Despite the widespread use of image augmentations across comput…
Re-ReND: Real-time Rendering of NeRFs across Devices
Sara Rojas, Jesus Zarzar, Juan Camilo Perez +4
This paper proposes a novel approach for rendering a pre-trained Neural Radiance Field (NeRF) in real-time on resource-constrained devices. We introduce Re-ReND, a method enabling…
SegNeRF: 3D Part Segmentation with Neural Radiance Fields
Jesus Zarzar, Sara Rojas, Silvio Giancola +1
Recent advances in Neural Radiance Fields (NeRF) boast impressive performances for generative tasks such as novel view synthesis and 3D reconstruction. Methods based on neural radi…
PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement
Jesus Zarzar, Silvio Giancola, Bernard Ghanem
In autonomous driving pipelines, perception modules provide a visual understanding of the surrounding road scene. Among the perception tasks, vehicle detection is of paramount impo…
Leveraging Shape Completion for 3D Siamese Tracking
Silvio Giancola, Jesus Zarzar, Bernard Ghanem
Point clouds are challenging to process due to their sparsity, therefore autonomous vehicles rely more on appearance attributes than pure geometric features. However, 3D LIDAR perc…
Efficient Bird Eye View Proposals for 3D Siamese Tracking
Jesus Zarzar, Silvio Giancola, Bernard Ghanem
Tracking vehicles in LIDAR point clouds is a challenging task due to the sparsity of the data and the dense search space. The lack of structure in point clouds impedes the use of c…