Yixin Zhuang's starred repositories
nerf-navigation
Code for the Nerf Navigation Paper. Implements a trajectory optimiser and state estimator which use NeRFs as an environment representation
AI4Animation
Bringing Characters to Life with Computer Brains in Unity
awesome-NeRF
A curated list of awesome neural radiance fields papers
fourier-feature-networks
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
awesome-implicit-representations
A curated list of resources on implicit neural representations.
Awsome_Deep_Geometry_Learning
A list of resources about deep learning solutions on 3D shape processing
awesome-visual-localization
A curated list of awesome visual localization research works.
Awesome-Implicit-NeRF-Robotics
A comprehensive list of Implicit Representations and NeRF papers relating to Robotics/RL domain, including papers, codes, and related websites
neural-tangents
Fast and Easy Infinite Neural Networks in Python
3D-Vision-and-Touch
When told to understand the shape of a new object, the most instinctual approach is to pick it up and inspect it with your hand and eyes in tandem. Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to fusing vision and touch, which leverages advances in graph convolutional networks. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single-modality baselines, especially when the object is occluded by the hand touching it; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) reconstruction quality boosts with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.
deep-geometric-prior
The reference implementaiton for the paper "Deep Geometric Prior for Surface Reconstruction"
nerf-pytorch
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.
unsupervised_co_part_segmentation
[ICML2021] Unsupervised Co-part Segmentation through Assembly
3D-Shape-Analysis-Paper-List
A list of recent papers, libraries and datasets about 3D shape/scene analysis (by topics, updating).
differentiable-point-clouds
The reference implementation of "Unsupervised Learning of Shape and Pose with Differentiable Point Clouds"
Generation3D
3D Shape Generation Baselines in PyTorch.