8 citations · 14 across the 9 of their papers we have counts for
10 papers
Do You Know Where Your Camera Is? View-Invariant Policy Learning with Camera Conditioning
Tianchong Jiang, Jingtian Ji, Xiangshan Tan +4
We study view-invariant imitation learning by explicitly conditioning policies on camera extrinsics. Using Plucker embeddings of per-pixel rays, we show that conditioning on extrin…
Embodied Spatial Intelligence: from Implicit Scene Modeling to Spatial Reasoning
Jiading Fang
This thesis introduces "Embodied Spatial Intelligence" to address the challenge of creating robots that can perceive and act in the real world based on natural language instruction…
SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting
Shengjie Lin, Jiading Fang, Muhammad Zubair Irshad +4
Reconstructing articulated objects prevalent in daily environments is crucial for applications in augmented/virtual reality and robotics. However, existing methods face scalability…
Transcrib3D: 3D Referring Expression Resolution through Large Language Models
Jiading Fang, Xiangshan Tan, Shengjie Lin +6
If robots are to work effectively alongside people, they must be able to interpret natural language references to objects in their 3D environment. Understanding 3D referring expres…
MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models
Peng Ding, Jiading Fang, Peng Li +6
Large language models such as ChatGPT and GPT-4 have recently achieved astonishing performance on a variety of natural language processing tasks. In this paper, we propose MANGO, a…
Statler: State-Maintaining Language Models for Embodied Reasoning
Takuma Yoneda, Jiading Fang, Peng Li +7
There has been a significant research interest in employing large language models to empower intelligent robots with complex reasoning. Existing work focuses on harnessing their ab…
NeRFuser: Large-Scale Scene Representation by NeRF Fusion
Jiading Fang, Shengjie Lin, Igor Vasiljevic +5
A practical benefit of implicit visual representations like Neural Radiance Fields (NeRFs) is their memory efficiency: large scenes can be efficiently stored and shared as small ne…
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields
Vitor Guizilini, Igor Vasiljevic, Jiading Fang +4
Differentiable volumetric rendering is a powerful paradigm for 3D reconstruction and novel view synthesis. However, standard volume rendering approaches struggle with degenerate ge…
Depth Field Networks for Generalizable Multi-view Scene Representation
Vitor Guizilini, Igor Vasiljevic, Jiading Fang +4
Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matchi…
Self-Supervised Camera Self-Calibration from Video
Jiading Fang, Igor Vasiljevic, Vitor Guizilini +4
Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration i…