2.4k citations · 6.1k across the 52 of their papers we have counts for
51 papers · 1 filter
Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting
Fangyin Wei, Thomas Funkhouser, Szymon Rusinkiewicz
Removing clutter from scenes is essential in many applications, ranging from privacy-concerned content filtering to data augmentation. In this work, we present an automatic system…
Nerflets: Local Radiance Fields for Efficient Structure-Aware 3D Scene Representation from 2D Supervision
Xiaoshuai Zhang, Abhijit Kundu, Thomas Funkhouser +3
We address efficient and structure-aware 3D scene representation from images. Nerflets are our key contribution -- a set of local neural radiance fields that together represent a s…
Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition
Hong-Xing Yu, Michelle Guo, Alireza Fathi +5
Photorealistic object appearance modeling from 2D images is a constant topic in vision and graphics. While neural implicit methods (such as Neural Radiance Fields) have shown high-…
OpenScene: 3D Scene Understanding with Open Vocabularies
Songyou Peng, Kyle Genova, Chiyu "Max" Jiang +3
Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision. We propose OpenScene, an alternative approach where a…
FutureHuman3D: Forecasting Complex Long-Term 3D Human Behavior from Video Observations
Christian Diller, Thomas Funkhouser, Angela Dai
We present a generative approach to forecast long-term future human behavior in 3D, requiring only weak supervision from readily available 2D human action data. This is a fundament…
MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures
Zhiqin Chen, Thomas Funkhouser, Peter Hedman +1
Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algori…
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation
Abhijit Kundu, Kyle Genova, Xiaoqi Yin +6
We present Panoptic Neural Fields (PNF), an object-aware neural scene representation that decomposes a scene into a set of objects (things) and background (stuff). Each object is r…
Neural Dual Contouring
Zhiqin Chen, Andrea Tagliasacchi, Thomas Funkhouser +1
We introduce neural dual contouring (NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC). Like traditional DC, it produces exactly one vertex per…
Revisiting 3D Object Detection From an Egocentric Perspective
Boyang Deng, Charles R. Qi, Mahyar Najibi +3
3D object detection is a key module for safety-critical robotics applications such as autonomous driving. For these applications, we care most about how the detections affect the e…
Urban Radiance Fields
Konstantinos Rematas, Andrew Liu, Pratul P. Srinivasan +4
The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environ…
Learning 3D Semantic Segmentation with only 2D Image Supervision
Kyle Genova, Xiaoqi Yin, Abhijit Kundu +6
With the recent growth of urban mapping and autonomous driving efforts, there has been an explosion of raw 3D data collected from terrestrial platforms with lidar scanners and colo…
Multiresolution Deep Implicit Functions for 3D Shape Representation
Zhang Chen, Yinda Zhang, Kyle Genova +7
We introduce Multiresolution Deep Implicit Functions (MDIF), a hierarchical representation that can recover fine geometry detail, while being able to perform global operations such…
Contrastive Multimodal Fusion with TupleInfoNCE
Yunze Liu, Qingnan Fan, Shanghang Zhang +3
This paper proposes a method for representation learning of multimodal data using contrastive losses. A traditional approach is to contrast different modalities to learn the inform…
IBRNet: Learning Multi-View Image-Based Rendering
Qianqian Wang, Zhicheng Wang, Kyle Genova +6
We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views. The core of our method is a network architecture that includes a m…
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding
Yunze Liu, Li Yi, Shanghang Zhang +3
Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used a…
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…
Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments
Siyan Dong, Qingnan Fan, He Wang +5
Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc. Recent advances estimate the camera pose via optimization…
Learning to Infer Semantic Parameters for 3D Shape Editing
Fangyin Wei, Elena Sizikova, Avneesh Sud +2
Many applications in 3D shape design and augmentation require the ability to make specific edits to an object's semantic parameters (e.g., the pose of a person's arm or the length…
Forecasting Characteristic 3D Poses of Human Actions
Christian Diller, Thomas Funkhouser, Angela Dai
We propose the task of forecasting characteristic 3d poses: from a short sequence observation of a person, predict a future 3d pose of that person in a likely action-defining, char…
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…
Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds
Li Yi, Boqing Gong, Thomas Funkhouser
We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors.…