35 citations · 56 across the 15 of their papers we have counts for
6 papers · 1 filter
HouseLayout3D: A Benchmark and Training-Free Baseline for 3D Layout Estimation in the Wild
Valentin Bieri, Marie-Julie Rakotosaona, Keisuke Tateno +2
Current 3D layout estimation models are primarily trained on synthetic datasets containing simple single room or single floor environments. As a consequence, they cannot natively h…
Learning Neural Exposure Fields for View Synthesis
Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona +5
Recent advances in neural scene representations have led to unprecedented quality in 3D reconstruction and view synthesis. Despite achieving high-quality results for common benchma…
AnyUp: Universal Feature Upsampling
Thomas Wimmer, Prune Truong, Marie-Julie Rakotosaona +4
We introduce AnyUp, a method for feature upsampling that can be applied to any vision feature at any resolution, without encoder-specific training. Existing learning-based upsample…
Masks make discriminative models great again!
Tianshi Cao, Marie-Julie Rakotosaona, Ben Poole +2
We present Image2GS, a novel approach that addresses the challenging problem of reconstructing photorealistic 3D scenes from a single image by focusing specifically on the image-to…
LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering
Jonas Kulhanek, Marie-Julie Rakotosaona, Fabian Manhardt +5
In this work, we present a novel level-of-detail (LOD) method for 3D Gaussian Splatting that enables real-time rendering of large-scale scenes on memory-constrained devices. Our ap…
Text To 3D Object Generation For Scalable Room Assembly
Sonia Laguna, Alberto Garcia-Garcia, Marie-Julie Rakotosaona +3
Modern machine learning models for scene understanding, such as depth estimation and object tracking, rely on large, high-quality datasets that mimic real-world deployment scenario…