activity
20172021
most citedMarrNet: 3D Shape Reconstruction via 2.5D Sketches

237 citations · 238 across the 2 of their papers we have counts for

collaborators

7 papers

cs.LG20211 cited

Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate

Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz +1

In design, fabrication, and control problems, we are often faced with the task of synthesis, in which we must generate an object or configuration that satisfies a set of constraint…

cs.RO2020

Spatial Action Maps for Mobile Manipulation

Jimmy Wu, Xingyuan Sun, Andy Zeng +4

Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e.g., step forward, turn left, turn right, etc.) from im…

cs.LG2019

A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation

Runzhe Yang, Xingyuan Sun, Karthik Narasimhan

We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim…

cs.CV2019

Learning to Infer and Execute 3D Shape Programs

Yonglong Tian, Andrew Luo, Xingyuan Sun +4

Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape struct…

cs.CV2018

3D Shape Perception from Monocular Vision, Touch, and Shape Priors

Shaoxiong Wang, Jiajun Wu, Xingyuan Sun +4

Perceiving accurate 3D object shape is important for robots to interact with the physical world. Current research along this direction has been primarily relying on visual observat…

cs.CV2018

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

Xingyuan Sun, Jiajun Wu, Xiuming Zhang +5

We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pix…