Search Results for author:
Found 8 papers, 5 papers with code
NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields
Given the capabilities of neural fields in densely representing a 3D scene from 2D images, we ask the question: Can we scale their self-supervised pretraining, specifically using masked autoencoders, to generate effective 3D representations from posed RGB images.
DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose Estimation
This paper addresses the challenging problem of category-level pose estimation.
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects
In this work, we address the challenging task of 3D object recognition without the reliance on real-world 3D labeled data.
NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes
NeO 360's representation allows us to learn from a large collection of unbounded 3D scenes while offering generalizability to new views and novel scenes from as few as a single image during inference.
Ranked #1 on
Generalizable Novel View Synthesis
on NERDS 360
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation.
ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
A novel disentangled shape and appearance database of priors is first learned to embed objects in their respective shape and appearance space.
3D Shape Reconstruction From A Single 2D Image
6D Pose Estimation
+4
CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation
This paper studies the complex task of simultaneous multi-object 3D reconstruction, 6D pose and size estimation from a single-view RGB-D observation.
Ranked #1 on
6D Pose Estimation using RGBD
on CAMERA25
SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments
This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments, which requires an autonomous agent to follow natural language instructions in unseen environments.
