4 citations · 14 across the 15 of their papers we have counts for
18 papers · 1 filter
HorizonWeaver: Generalizable Multi-Level Semantic Editing for Driving Scenes
Mauricio Soroco, Francesco Pittaluga, Zaid Tasneem +5
Ensuring safety in autonomous driving requires scalable generation of realistic, controllable driving scenes beyond what real-world testing provides. Yet existing instruction guide…
AutoScape: Geometry-Consistent Long-Horizon Scene Generation
Jiacheng Chen, Ziyu Jiang, Mingfu Liang +5
This paper proposes AutoScape, a long-horizon driving scene generation framework. At its core is a novel RGB-D diffusion model that iteratively generates sparse, geometrically cons…
iFinder: Structured Zero-Shot Vision-Based LLM Grounding for Dash-Cam Video Reasoning
Manyi Yao, Bingbing Zhuang, Sparsh Garg +4
Grounding large language models (LLMs) in domain-specific tasks like post-hoc dash-cam driving video analysis is challenging due to their general-purpose training and lack of struc…
Drive-1-to-3: Enriching Diffusion Priors for Novel View Synthesis of Real Vehicles
Chuang Lin, Bingbing Zhuang, Shanlin Sun +3
The recent advent of large-scale 3D data, e.g. Objaverse, has led to impressive progress in training pose-conditioned diffusion models for novel view synthesis. However, due to the…
Instantaneous Perception of Moving Objects in 3D
Di Liu, Bingbing Zhuang, Dimitris N. Metaxas +1
The perception of 3D motion of surrounding traffic participants is crucial for driving safety. While existing works primarily focus on general large motions, we contend that the in…
LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes
Shanlin Sun, Bingbing Zhuang, Ziyu Jiang +3
Photorealistic simulation plays a crucial role in applications such as autonomous driving, where advances in neural radiance fields (NeRFs) may allow better scalability through the…
LDP-Feat: Image Features with Local Differential Privacy
Francesco Pittaluga, Bingbing Zhuang
Modern computer vision services often require users to share raw feature descriptors with an untrusted server. This presents an inherent privacy risk, as raw descriptors may be use…
NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization
Zhixiang Min, Bingbing Zhuang, Samuel Schulter +3
Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature. Estimating 3D coordinates for each pixel on the object surface ho…
MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation
Inkyu Shin, Yi-Hsuan Tsai, Bingbing Zhuang +5
Test-time adaptation approaches have recently emerged as a practical solution for handling domain shift without access to the source domain data. In this paper, we propose and expl…
Learning Cross-modal Contrastive Features for Video Domain Adaptation
Donghyun Kim, Yi-Hsuan Tsai, Bingbing Zhuang +4
Learning transferable and domain adaptive feature representations from videos is important for video-relevant tasks such as action recognition. Existing video domain adaptation met…
Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty
Bingbing Zhuang, Manmohan Chandraker
Learning methods for relative camera pose estimation have been developed largely in isolation from classical geometric approaches. The question of how to integrate predictions from…
Weakly But Deeply Supervised Occlusion-Reasoned Parametric Road Layouts
Buyu Liu, Bingbing Zhuang, Manmohan Chandraker
We propose an end-to-end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion-reasoned layouts in perspective space as well as a…
Image Stitching and Rectification for Hand-Held Cameras
Bingbing Zhuang, Quoc-Huy Tran
In this paper, we derive a new differential homography that can account for the scanline-varying camera poses in Rolling Shutter (RS) cameras, and demonstrate its application to ca…
Understanding Road Layout from Videos as a Whole
Buyu Liu, Bingbing Zhuang, Samuel Schulter +2
In this paper, we address the problem of inferring the layout of complex road scenes from video sequences. To this end, we formulate it as a top-view road attributes prediction pro…
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
Lokender Tiwari, Pan Ji, Quoc-Huy Tran +3
Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely…
Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM
Bingbing Zhuang, Quoc-Huy Tran, Pan Ji +3
Self-calibration of camera intrinsics and radial distortion has a long history of research in the computer vision community. However, it remains rare to see real applications of su…
Rolling-Shutter-Aware Differential SfM and Image Rectification
Bingbing Zhuang, Loong-Fah Cheong, Gim Hee Lee
In this paper, we develop a modified differential Structure from Motion (SfM) algorithm that can estimate relative pose from two consecutive frames despite of Rolling Shutter (RS)…
Baseline Desensitizing In Translation Averaging
Bingbing Zhuang, Loong-Fah Cheong, Gim Hee Lee
Many existing translation averaging algorithms are either sensitive to disparate camera baselines and have to rely on extensive preprocessing to improve the observed Epipolar Geome…