most citedNeRFuser: Large-Scale Scene Representation by NeRF Fusion

3 citations · 7 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2023

FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects

Mayank Lunayach, Sergey Zakharov, Dian Chen +3

In this work, we address the challenging task of 3D object recognition without the reliance on real-world 3D labeled data. Our goal is to predict the 3D shape, size, and 6D pose of…

cs.CV20231 cited

ShaSTA-Fuse: Camera-LiDAR Sensor Fusion to Model Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking

Tara Sadjadpour, Rares Ambrus, Jeannette Bohg

3D multi-object tracking (MOT) is essential for an autonomous mobile agent to safely navigate a scene. In order to maximize the perception capabilities of the autonomous agent, we…

cs.CV2023

NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes

Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu +5

Recent implicit neural representations have shown great results for novel view synthesis. However, existing methods require expensive per-scene optimization from many views hence l…

cs.CV2023

Robust Self-Supervised Extrinsic Self-Calibration

Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini +2

Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth esti…

cs.CV20233 cited

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…

cs.CV20231 cited

Viewpoint Equivariance for Multi-View 3D Object Detection

Dian Chen, Jie Li, Vitor Guizilini +2

3D object detection from visual sensors is a cornerstone capability of robotic systems. State-of-the-art methods focus on reasoning and decoding object bounding boxes from multi-vi…