79 citations · 338 across the 27 of their papers we have counts for
13 papers · 1 filter
Improving Unsupervised Visual Program Inference with Code Rewriting Families
Aditya Ganeshan, R. Kenny Jones, Daniel Ritchie
Programs offer compactness and structure that makes them an attractive representation for visual data. We explore how code rewriting can be used to improve systems for inferring pr…
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly
Xianghao Xu, Paul Guerrero, Matthew Fisher +2
Representing a 3D shape with a set of primitives can aid perception of structure, improve robotic object manipulation, and enable editing, stylization, and compression of 3D shapes…
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language
Aditya Sanghi, Rao Fu, Vivian Liu +5
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We intr…
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model
Rao Fu, Xiao Zhan, Yiwen Chen +2
We present ShapeCrafter, a neural network for recursive text-conditioned 3D shape generation. Existing methods to generate text-conditioned 3D shapes consume an entire text prompt…
SHRED: 3D Shape Region Decomposition with Learned Local Operations
R. Kenny Jones, Aalia Habib, Daniel Ritchie
We present SHRED, a method for 3D SHape REgion Decomposition. SHRED takes a 3D point cloud as input and uses learned local operations to produce a segmentation that approximates fi…
Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms
Kai Wang, Xianghao Xu, Leon Lei +5
Realistic 3D indoor scene datasets have enabled significant recent progress in computer vision, scene understanding, autonomous navigation, and 3D reconstruction. But the scale, di…
Learning to Infer Kinematic Hierarchies for Novel Object Instances
Hameed Abdul-Rashid, Miles Freeman, Ben Abbatematteo +2
Manipulating an articulated object requires perceiving itskinematic hierarchy: its parts, how each can move, and howthose motions are coupled. Previous work has explored per-ceptio…
Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing
Purvi Goel, Loudon Cohen, James Guesman +3
Reconstructing object geometry and material from multiple views typically requires optimization. Differentiable path tracing is an appealing framework as it can reproduce complex a…
PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions
R. Kenny Jones, Homer Walke, Daniel Ritchie
Inferring programs which generate 2D and 3D shapes is important for reverse engineering, editing, and more. Training models to perform this task is complicated because paired (shap…
GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image Translation
Wallace Lira, Johannes Merz, Daniel Ritchie +2
We introduce GANHopper, an unsupervised image-to-image translation network that transforms images gradually between two domains, through multiple hops. Instead of executing transla…
Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models
Daniel Ritchie, Kai Wang, Yu-an Lin
We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based represe…
Improving Shape Deformation in Unsupervised Image-to-Image Translation
Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie +2
Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape chan…
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
Angela Dai, Daniel Ritchie, Martin Bokeloh +3
We introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D scan of a scene as input and predicting a complete 3D model along with per-voxel semantic labels…