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20142023
most citedPointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

2.9k citations · 10.7k across the 136 of their papers we have counts for

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Showing 2022Show all

18 papers · 1 filter

cs.CV2022

LADIS: Language Disentanglement for 3D Shape Editing

Ian Huang, Panos Achlioptas, Tianyi Zhang +3

Natural language interaction is a promising direction for democratizing 3D shape design. However, existing methods for text-driven 3D shape editing face challenges in producing dec…

cs.CV2022★ 3 cited

ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction

Zhen Wang, Shijie Zhou, Jeong Joon Park +5

This work introduces alternating latent topologies (ALTO) for high-fidelity reconstruction of implicit 3D surfaces from noisy point clouds. Previous work identifies that the spatia…

cs.CV2022★ 3 cited

NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors

Congyue Deng, Chiyu "Max'' Jiang, Charles R. Qi +4

2D-to-3D reconstruction is an ill-posed problem, yet humans are good at solving this problem due to their prior knowledge of the 3D world developed over years. Driven by this obser…

cs.CV2022★ 3 cited

MetaCLUE: Towards Comprehensive Visual Metaphors Research

Arjun R. Akula, Brendan Driscoll, Pradyumna Narayana +9

Creativity is an indispensable part of human cognition and also an inherent part of how we make sense of the world. Metaphorical abstraction is fundamental in communicating creativ…

cs.CV2022★ 3 cited

SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene

Minjung Son, Jeong Joon Park, Leonidas Guibas +1

Generative models have shown great promise in synthesizing photorealistic 3D objects, but they require large amounts of training data. We introduce SinGRAF, a 3D-aware generative m…

cs.CV2022

Breaking the Symmetry: Resolving Symmetry Ambiguities in Equivariant Neural Networks

Sidhika Balachandar, Adrien Poulenard, Congyue Deng +1

Equivariant networks have been adopted in many 3-D learning areas. Here we identify a fundamental limitation of these networks: their ambiguity to symmetries. Equivariant networks…