activity
20192022
most citedThe Replica Dataset: A Digital Replica of Indoor Spaces

384 citations · 412 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2022★ 1 cited

ERF: Explicit Radiance Field Reconstruction From Scratch

Samir Aroudj, Steven Lovegrove, Eddy Ilg +3

We propose a novel explicit dense 3D reconstruction approach that processes a set of images of a scene with sensor poses and calibrations and estimates a photo-real digital model.…

cs.CV2021★ 2 cited

Identity-Disentangled Neural Deformation Model for Dynamic Meshes

Binbin Xu, Lingni Ma, Yuting Ye +3

Neural shape models can represent complex 3D shapes with a compact latent space. When applied to dynamically deforming shapes such as the human hands, however, they would need to p…

cs.CV2020★ 1 cited

STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering

Wentao Yuan, Zhaoyang Lv, Tanner Schmidt +1

We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotati…

cs.CV2020★ 13 cited

FroDO: From Detections to 3D Objects

Kejie Li, Martin Rünz, Meng Tang +8

Object-oriented maps are important for scene understanding since they jointly capture geometry and semantics, allow individual instantiation and meaningful reasoning about objects.…

cs.CV2020★ 3 cited

Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction

Rohan Chabra, Jan Eric Lenssen, Eddy Ilg +4

Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a…

cs.CV2019★ 384 cited

The Replica Dataset: A Digital Replica of Indoor Spaces

Julian Straub, Thomas Whelan, Lingni Ma +27

We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-d…

cs.CV2019★ 8 cited

DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation

Jeong Joon Park, Peter Florence, Julian Straub +2

Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-o…