6 citations · 9 across the 8 of their papers we have counts for
11 papers
Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration
Lily Goli, Justin Kerr, Daniele Reda +3
Exploration is a prerequisite for learning useful behaviors in sparse-reward, long-horizon tasks, particularly within 3D environments. Curiosity-driven reinforcement learning addre…
FullCircle: Effortless 3D Reconstruction from Casual 360 Captures
Yalda Foroutan, Ipek Oztas, Daniel Rebain +4
Radiance fields have emerged as powerful tools for 3D scene reconstruction. However, casual capture remains challenging due to the narrow field of view of perspective cameras, whic…
Grow with the Flow: 4D Reconstruction of Growing Plants with Gaussian Flow Fields
Weihan Luo, Lily Goli, Sherwin Bahmani +3
Modeling the time-varying 3D appearance of plants during growth poses unique challenges: unlike most dynamic scenes, plants continuously generate new geometry as they expand, branc…
Flux4D: Flow-based Unsupervised 4D Reconstruction
Jingkang Wang, Henry Che, Yun Chen +4
Reconstructing large-scale dynamic scenes from visual observations is a fundamental challenge in computer vision, with critical implications for robotics and autonomous systems. Wh…
Hierarchical Transformers for Unsupervised 3D Shape Abstraction
Aditya Vora, Lily Goli, Andrea Tagliasacchi +1
We introduce HiT, a novel hierarchical neural field representation for 3D shapes that learns general hierarchies in a coarse-to-fine manner across different shape categories in an…
3D Gaussian Flats: Hybrid 2D/3D Photometric Scene Reconstruction
Maria Taktasheva, Lily Goli, Alessandro Fiorini +3
Recent advances in radiance fields and novel view synthesis enable creation of realistic digital twins from photographs. However, current methods struggle with flat, texture-less s…
RoMo: Robust Motion Segmentation Improves Structure from Motion
Lily Goli, Sara Sabour, Mark Matthews +6
There has been extensive progress in the reconstruction and generation of 4D scenes from monocular casually-captured video. While these tasks rely heavily on known camera poses, th…
SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
Sara Sabour, Lily Goli, George Kopanas +6
3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications.However…
BANF: Band-limited Neural Fields for Levels of Detail Reconstruction
Ahan Shabanov, Shrisudhan Govindarajan, Cody Reading +4
Largely due to their implicit nature, neural fields lack a direct mechanism for filtering, as Fourier analysis from discrete signal processing is not directly applicable to these r…
Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields
Lily Goli, Cody Reading, Silvia Sellán +2
Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation, but learning from multiview images faces inherent uncertainties. Current…
nerf2nerf: Pairwise Registration of Neural Radiance Fields
Lily Goli, Daniel Rebain, Sara Sabour +2
We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i.e. ICP) to operate on Neural Radiance Fields (Ne…