183 citations · 340 across the 13 of their papers we have counts for
18 papers · 1 filter
FFVO: A Feedforward Pose Decoder for Long-Horizon Visual Odometry
Meng-Li Shih, Shih-Yang Su, Yuliang Zou +7
Stable and reliable 4D spatial understanding is fundamental for autonomous driving systems. While feedforward reconstruction networks can estimate camera motion and 3D structure in…
Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving
Jiahao Wang, Bo Sun, Yijing Bai +12
Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle (AV) fleets, while high-fidel…
SceneCrafter: Controllable Multi-View Driving Scene Editing
Zehao Zhu, Yuliang Zou, Chiyu Max Jiang +9
Simulation is crucial for developing and evaluating autonomous vehicle (AV) systems. Recent literature builds on a new generation of generative models to synthesize highly realisti…
Orchid: Image Latent Diffusion for Joint Appearance and Geometry Generation
Akshay Krishnan, Xinchen Yan, Vincent Casser +1
We introduce Orchid, a unified latent diffusion model that learns a joint appearance-geometry prior to generate color, depth, and surface normal images in a single diffusion proces…
Instance Segmentation with Cross-Modal Consistency
Alex Zihao Zhu, Vincent Casser, Reza Mahjourian +2
Segmenting object instances is a key task in machine perception, with safety-critical applications in robotics and autonomous driving. We introduce a novel approach to instance seg…
LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection
Wei-Chih Hung, Vincent Casser, Henrik Kretzschmar +2
The 3D Average Precision (3D AP) relies on the intersection over union between predictions and ground truth objects. However, camera-only detectors have limited depth accuracy, whi…
Block-NeRF: Scalable Large Scene Neural View Synthesis
Matthew Tancik, Vincent Casser, Xinchen Yan +5
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. Specifically, we demonstrate that when scaling NeRF to render city-scale sce…
4D-Net for Learned Multi-Modal Alignment
AJ Piergiovanni, Vincent Casser, Michael S. Ryoo +1
We present 4D-Net, a 3D object detection approach, which utilizes 3D Point Cloud and RGB sensing information, both in time. We are able to incorporate the 4D information by perform…
Unsupervised Monocular Depth Learning in Dynamic Scenes
Hanhan Li, Ariel Gordon, Hang Zhao +2
We present a method for jointly training the estimation of depth, ego-motion, and a dense 3D translation field of objects relative to the scene, with monocular photometric consiste…
Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability
Anelise Newman, Camilo Fosco, Vincent Casser +3
A key capability of an intelligent system is deciding when events from past experience must be remembered and when they can be forgotten. Towards this goal, we develop a predictive…
Predicting Visual Importance Across Graphic Design Types
Camilo Fosco, Vincent Casser, Amish Kumar Bedi +3
This paper introduces a Unified Model of Saliency and Importance (UMSI), which learns to predict visual importance in input graphic designs, and saliency in natural images, along w…
Taskology: Utilizing Task Relations at Scale
Yao Lu, Sören Pirk, Jan Dlabal +6
Many computer vision tasks address the problem of scene understanding and are naturally interrelated e.g. object classification, detection, scene segmentation, depth estimation, et…
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
Vincent Casser, Soeren Pirk, Reza Mahjourian +1
We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion. More specifically, we model the motion of…
Fast Mitochondria Detection for Connectomics
Vincent Casser, Kai Kang, Hanspeter Pfister +1
High-resolution connectomics data allows for the identification of dysfunctional mitochondria which are linked to a variety of diseases such as autism or bipolar. However, manual a…
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Vincent Casser, Soeren Pirk, Reza Mahjourian +1
Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and…
OIL: Observational Imitation Learning
Guohao Li, Matthias Müller, Vincent Casser +3
Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images. However, t…
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation
Matthias Müller, Vincent Casser, Neil Smith +2
Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years. However, teaching UAVs to fly in challenging environmen…
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications
Matthias Müller, Vincent Casser, Jean Lahoud +2
We present a photo-realistic training and evaluation simulator (Sim4CV) with extensive applications across various fields of computer vision. Built on top of the Unreal Engine, the…