211 citations · 582 across the 19 of their papers we have counts for
7 papers · 1 filter
Object-Centric Neural Scene Rendering
Michelle Guo, Alireza Fathi, Jiajun Wu +1
We present a method for composing photorealistic scenes from captured images of objects. Our work builds upon neural radiance fields (NeRFs), which implicitly model the volumetric…
Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection
Yue Wang, Alireza Fathi, Jiajun Wu +2
A common dilemma in 3D object detection for autonomous driving is that high-quality, dense point clouds are only available during training, but not testing. We use knowledge distil…
Virtual Multi-view Fusion for 3D Semantic Segmentation
Abhijit Kundu, Xiaoqi Yin, Alireza Fathi +4
Semantic segmentation of 3D meshes is an important problem for 3D scene understanding. In this paper we revisit the classic multiview representation of 3D meshes and study several…
Pillar-based Object Detection for Autonomous Driving
Yue Wang, Alireza Fathi, Abhijit Kundu +4
We present a simple and flexible object detection framework optimized for autonomous driving. Building on the observation that point clouds in this application are extremely sparse…
An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds
Rui Huang, Wanyue Zhang, Abhijit Kundu +4
Detecting objects in 3D LiDAR data is a core technology for autonomous driving and other robotics applications. Although LiDAR data is acquired over time, most of the 3D object det…
DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes
Mahyar Najibi, Guangda Lai, Abhijit Kundu +7
We propose DOPS, a fast single-stage 3D object detection method for LIDAR data. Previous methods often make domain-specific design decisions, for example projecting points into a b…