Search Results for author:
Found 16 papers, 5 papers with code
Gaga: Group Any Gaussians via 3D-aware Memory Bank
We introduce Gaga, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot segmentation models.
NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis
This interaction field guides the sampling of an object-conditioned human motion diffusion model, so as to encourage plausible contacts and affordance semantics.
Learning a Diffusion Prior for NeRFs
Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D representation for objects and scenes derived from 2D data.
NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes
Obtaining 3D meshes from neural radiance fields still remains an open challenge since NeRFs are optimized for view synthesis, not enforcing an accurate underlying geometry on the radiance field.
Nerflets: Local Radiance Fields for Efficient Structure-Aware 3D Scene Representation from 2D Supervision
We address efficient and structure-aware 3D scene representation from images.
im2nerf: Image to Neural Radiance Field in the Wild
We take a step towards addressing this shortcoming by introducing a model that encodes the input image into a disentangled object representation that contains a code for object shape, a code for object appearance, and an estimated camera pose from which the object image is captured.
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation
Our model builds a panoptic radiance field representation of any scene from just color images.
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
Learning 3D Semantic Segmentation with only 2D Image Supervision
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 color cameras.
Ranked #8 on
LIDAR Semantic Segmentation
on nuScenes
Virtual Multi-view Fusion for 3D Semantic Segmentation
Features from multiple per view predictions are finally fused on 3D mesh vertices to predict mesh semantic segmentation labels.
Ranked #12 on
Semantic Segmentation
on ScanNet
An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds
We use a U-Net style 3D sparse convolution network to extract features for each frame's LiDAR point-cloud.
Pillar-based Object Detection for Autonomous Driving
We present a simple and flexible object detection framework optimized for autonomous driving.
DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes
In contrast, we propose a general-purpose method that works on both indoor and outdoor scenes.
Adversarial Texture Optimization from RGB-D Scans
In this work, we present a novel approach for color texture generation using a conditional adversarial loss obtained from weakly-supervised views.
3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare
Our method produces a compact 3D representation of the scene, which can be readily used for applications like autonomous driving.
Ranked #3 on
Vehicle Pose Estimation
on KITTI Cars Hard
(using extra training data)
Feature Space Optimization for Semantic Video Segmentation
We present an approach to long-range spatio-temporal regularization in semantic video segmentation.
