22 citations · 55 across the 22 of their papers we have counts for
27 papers
RoboCloth: A Large-Scale Real Cloth Material Dataset for Neural Reflectance Reconstruction
Zhen Li, Haoran Qin, Francesco Di Sario +2
We present RoboCloth, a dataset of 500 real cloth materials captured under flexible, calibrated camera and lighting configurations, where both are robotically controlled during acq…
Semantic Foam: Unifying Spatial and Semantic Scene Decomposition
Amr Sharafeldin, Shrisudhan Govindarajan, Thomas Walker +4
Modern scene reconstruction methods, such as 3D Gaussian Splatting, deliver photo-realistic novel view synthesis at real-time speeds, yet their adoption in interactive graphics app…
Power Foam: Unifying Real-Time Differentiable Ray Tracing and Rasterization
Shrisudhan Govindarajan, Daniel Rebain, Dor Verbin +3
We introduce a differentiable 3D representation that unifies the ray tracing capabilities of foam-based ray tracing with the efficiency of modern rasterization pipelines. While pri…
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…
Nexels: Neurally-Textured Surfels for Real-Time Novel View Synthesis with Sparse Geometries
Victor Rong, Jan Held, Victor Chu +5
Though Gaussian splatting has achieved impressive results in novel view synthesis, it requires millions of primitives to model highly textured scenes, even when the geometry of the…
MeshSplatting: Differentiable Rendering with Opaque Meshes
Jan Held, Sanghyun Son, Renaud Vandeghen +7
Primitive-based splatting methods like 3D Gaussian Splatting have revolutionized novel view synthesis with real-time rendering. However, their point-based representations remain in…
Spherical Voronoi: Directional Appearance as a Differentiable Partition of the Sphere
Francesco Di Sario, Daniel Rebain, Dor Verbin +2
Radiance field methods (e.g. 3D Gaussian Splatting) have emerged as a powerful paradigm for novel view synthesis, yet their appearance modeling often relies on Spherical Harmonics…
Triangle Splatting+: Differentiable Rendering with Opaque Triangles
Jan Held, Renaud Vandeghen, Sanghyun Son +6
Reconstructing 3D scenes and synthesizing novel views has seen rapid progress in recent years. Neural Radiance Fields demonstrated that continuous volumetric radiance fields can ac…
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…
NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud Serialization
Zhen Li, Weiwei Sun, Shrisudhan Govindarajan +4
We present a novel approach to large-scale point cloud surface reconstruction by developing an efficient framework that converts an irregular point cloud into a signed distance fie…
Radiant Foam: Real-Time Differentiable Ray Tracing
Shrisudhan Govindarajan, Daniel Rebain, Kwang Moo Yi +1
Research on differentiable scene representations is consistently moving towards more efficient, real-time models. Recently, this has led to the popularization of splatting methods,…
LSE-NeRF: Learning Sensor Modeling Errors for Deblured Neural Radiance Fields with RGB-Event Stereo
Wei Zhi Tang, Daniel Rebain, Kostantinos G. Derpanis +1
We present a method for reconstructing a clear Neural Radiance Field (NeRF) even with fast camera motions. To address blur artifacts, we leverage both (blurry) RGB images and event…
Lagrangian Hashing for Compressed Neural Field Representations
Shrisudhan Govindarajan, Zeno Sambugaro, Akhmedkhan +7
We present Lagrangian Hashing, a representation for neural fields combining the characteristics of fast training NeRF methods that rely on Eulerian grids (i.e.~InstantNGP), with th…
Evaluating Alternatives to SFM Point Cloud Initialization for Gaussian Splatting
Yalda Foroutan, Daniel Rebain, Kwang Moo Yi +1
3D Gaussian Splatting has recently been embraced as a versatile and effective method for scene reconstruction and novel view synthesis, owing to its high-quality results and compat…
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…
3D Gaussian Splatting as Markov Chain Monte Carlo
Shakiba Kheradmand, Daniel Rebain, Gopal Sharma +6
While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, w…
Volumetric Rendering with Baked Quadrature Fields
Gopal Sharma, Daniel Rebain, Kwang Moo Yi +1
We propose a novel Neural Radiance Field (NeRF) representation for non-opaque scenes that enables fast inference by utilizing textured polygons. Despite the high-quality novel view…
Accelerating Neural Field Training via Soft Mining
Shakiba Kheradmand, Daniel Rebain, Gopal Sharma +4
We present an approach to accelerate Neural Field training by efficiently selecting sampling locations. While Neural Fields have recently become popular, it is often trained by uni…
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…
Attention Beats Concatenation for Conditioning Neural Fields
Daniel Rebain, Mark J. Matthews, Kwang Moo Yi +3
Neural fields model signals by mapping coordinate inputs to sampled values. They are becoming an increasingly important backbone architecture across many fields from vision and gra…
NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds
Weiwei Sun, Daniel Rebain, Renjie Liao +4
We introduce a method for instance proposal generation for 3D point clouds. Existing techniques typically directly regress proposals in a single feed-forward step, leading to inacc…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…
LOLNeRF: Learn from One Look
Daniel Rebain, Mark Matthews, Kwang Moo Yi +2
We present a method for learning a generative 3D model based on neural radiance fields, trained solely from data with only single views of each object. While generating realistic i…
Deep Medial Fields
Daniel Rebain, Ke Li, Vincent Sitzmann +3
Implicit representations of geometry, such as occupancy fields or signed distance fields (SDF), have recently re-gained popularity in encoding 3D solid shape in a functional form.…