Search Results for author:
Found 10 papers, 3 papers with code
$SE(3)$ Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation
Incorporating inductive bias by embedding geometric entities (such as rays) as input has proven successful in multi-view learning.
View-Invariant Policy Learning via Zero-Shot Novel View Synthesis
For practical application to diverse robotic data, these models must operate zero-shot, performing view synthesis on unseen tasks and environments.
ReFiNe: Recursive Field Networks for Cross-modal Multi-scene Representation
The common trade-offs of state-of-the-art methods for multi-shape representation (a single model "packing" multiple objects) involve trading modeling accuracy against memory and storage.
Zero-Shot Multi-Object Scene Completion
We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image.
DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose Estimation
This paper addresses the challenging problem of category-level pose estimation.
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
Multi-Object Manipulation via Object-Centric Neural Scattering Functions
Learned visual dynamics models have proven effective for robotic manipulation tasks.
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.
ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapes
Compact and accurate representations of 3D shapes are central to many perception and robotics tasks.
Online Descriptor Enhancement via Self-Labelling Triplets for Visual Data Association
We show that self-labelling challenging triplets--choosing positive examples separated by large temporal distances and negative examples close in the descriptor space--improves the quality of the learned descriptors for the multi-object tracking task.
