351 citations · 431 across the 31 of their papers we have counts for
33 papers
Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Video
Hongchi Xia, Zhi-Hao Lin, Wei-Chiu Ma +1
Creating high-quality and interactive virtual environments, such as games and simulators, often involves complex and costly manual modeling processes. In this paper, we present Vid…
BLINK: Multimodal Large Language Models Can See but Not Perceive
Xingyu Fu, Yushi Hu, Bangzheng Li +7
We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations. Most of the Blink tasks c…
Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects
Tianhang Cheng, Wei-Chiu Ma, Kaiyu Guan +2
Our world is full of identical objects (\emphe.g., cans of coke, cars of same model). These duplicates, when seen together, provide additional and strong cues for us to effectively…
LightSim: Neural Lighting Simulation for Urban Scenes
Ava Pun, Gary Sun, Jingkang Wang +5
Different outdoor illumination conditions drastically alter the appearance of urban scenes, and they can harm the performance of image-based robot perception systems if not seen du…
UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation
Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang +1
LiDAR provides accurate geometric measurements of the 3D world. Unfortunately, dense LiDARs are very expensive and the point clouds captured by low-beam LiDAR are often sparse. To…
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
Jingkang Wang, Sivabalan Manivasagam, Yun Chen +5
Realistic simulation is key to enabling safe and scalable development of % self-driving vehicles. A core component is simulating the sensors so that the entire autonomy system can…
UniSim: A Neural Closed-Loop Sensor Simulator
Ze Yang, Yun Chen, Jingkang Wang +4
Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV) a reality. It requires one to generate safety critical scenarios beyond what can be col…
What Happened 3 Seconds Ago? Inferring the Past with Thermal Imaging
Zitian Tang, Wenjie Ye, Wei-Chiu Ma +1
Inferring past human motion from RGB images is challenging due to the inherent uncertainty of the prediction problem. Thermal images, on the other hand, encode traces of past human…
VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection
Zhuoling Li, Chuanrui Zhang, Wei-Chiu Ma +5
In recent years, transformer-based detectors have demonstrated remarkable performance in 2D visual perception tasks. However, their performance in multi-view 3D object detection re…
Virtual Correspondence: Humans as a Cue for Extreme-View Geometry
Wei-Chiu Ma, Anqi Joyce Yang, Shenlong Wang +2
Recovering the spatial layout of the cameras and the geometry of the scene from extreme-view images is a longstanding challenge in computer vision. Prevailing 3D reconstruction alg…
NeurMiPs: Neural Mixture of Planar Experts for View Synthesis
Zhi-Hao Lin, Wei-Chiu Ma, Hao-Yu Hsu +2
We present Neural Mixtures of Planar Experts (NeurMiPs), a novel planar-based scene representation for modeling geometry and appearance. NeurMiPs leverages a collection of local pl…
BARF: Bundle-Adjusting Neural Radiance Fields
Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba +1
Neural Radiance Fields (NeRF) have recently gained a surge of interest within the computer vision community for its power to synthesize photorealistic novel views of real-world sce…
Deep Feedback Inverse Problem Solver
Wei-Chiu Ma, Shenlong Wang, Jiayuan Gu +3
We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn…
Deep Parametric Continuous Convolutional Neural Networks
Shenlong Wang, Simon Suo, Wei-Chiu Ma +2
Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This limits their applica…
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
Ze Yang, Shenlong Wang, Sivabalan Manivasagam +5
Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation.…
VideoClick: Video Object Segmentation with a Single Click
Namdar Homayounfar, Justin Liang, Wei-Chiu Ma +1
Annotating videos with object segmentation masks typically involves a two stage procedure of drawing polygons per object instance for all the frames and then linking them through t…
Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild
Shivam Duggal, Zihao Wang, Wei-Chiu Ma +4
Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and grap…
DAGMapper: Learning to Map by Discovering Lane Topology
Namdar Homayounfar, Wei-Chiu Ma, Justin Liang +3
One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost. Current attempts to automate this process typ…
Hierarchical Recurrent Attention Networks for Structured Online Maps
Namdar Homayounfar, Wei-Chiu Ma, Shrinidhi Kowshika Lakshmikanth +1
In this paper, we tackle the problem of online road network extraction from sparse 3D point clouds. Our method is inspired by how an annotator builds a lane graph, by first identif…
Convolutional Recurrent Network for Road Boundary Extraction
Justin Liang, Namdar Homayounfar, Wei-Chiu Ma +2
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper…
Recovering and Simulating Pedestrians in the Wild
Ze Yang, Siva Manivasagam, Ming Liang +3
Sensor simulation is a key component for testing the performance of self-driving vehicles and for data augmentation to better train perception systems. Typical approaches rely on a…
Conditional Entropy Coding for Efficient Video Compression
Jerry Liu, Shenlong Wang, Wei-Chiu Ma +4
We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. Unlike prior learning-based approaches, we…
Weakly-supervised 3D Shape Completion in the Wild
Jiayuan Gu, Wei-Chiu Ma, Sivabalan Manivasagam +5
3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned. Different from pr…
LevelSet R-CNN: A Deep Variational Method for Instance Segmentation
Namdar Homayounfar, Yuwen Xiong, Justin Liang +2
Obtaining precise instance segmentation masks is of high importance in many modern applications such as robotic manipulation and autonomous driving. Currently, many state of the ar…