Search Results for author:
Found 26 papers, 14 papers with code
EgoPoints: Advancing Point Tracking for Egocentric Videos
When fine-tuning point tracking methods on these sequences and evaluating on our annotated EgoPoints sequences, we improve CoTracker across all metrics, including the tracking accuracy $\delta^\star_{\text{avg}}$ by 2. 7 percentage points and accuracy on ReID sequences (ReID$\delta_{\text{avg}}$) by 2. 4 points.
Monocular Dynamic Gaussian Splatting is Fast and Brittle but Smooth Motion Helps
Gaussian splatting methods are emerging as a popular approach for converting multi-view image data into scene representations that allow view synthesis.
View-Consistent Hierarchical 3D Segmentation Using Ultrametric Feature Fields
In this work, we address the challenging task of lifting multi-granular and view-inconsistent image segmentations into a hierarchical and 3D-consistent representation.
ODIN: A Single Model for 2D and 3D Segmentation
The gap in performance between methods that consume posed images versus post-processed 3D point clouds has fueled the belief that 2D and 3D perception require distinct model architectures.
Ranked #1 on
3D Instance Segmentation
on ScanNet200
Refining Pre-Trained Motion Models
In the first stage, we use the pre-trained model to estimate motion in a video, and then select the subset of motion estimates which we can verify with cycle-consistency.
PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments
We introduce PACE (Pose Annotations in Cluttered Environments), a large-scale benchmark designed to advance the development and evaluation of pose estimation methods in cluttered scenarios.
Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models
This begs the question: can we re-purpose these large-scale pre-trained static image models for open-vocabulary video tracking?
Support-Set Context Matters for Bongard Problems
Current machine learning methods struggle to solve Bongard problems, which are a type of IQ test that requires deriving an abstract "concept" from a set of positive and negative "support" images, and then classifying whether or not a new query image depicts the key concept.
Ranked #2 on
Few-Shot Image Classification
on Bongard-HOI
(using extra training data)
PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking
Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion.
Ranked #1 on
Point Tracking
on TAP-Vid
TIDEE: Tidying Up Novel Rooms using Visuo-Semantic Commonsense Priors
We introduce TIDEE, an embodied agent that tidies up a disordered scene based on learned commonsense object placement and room arrangement priors.
Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?
Building 3D perception systems for autonomous vehicles that do not rely on high-density LiDAR is a critical research problem because of the expense of LiDAR systems compared to cameras and other sensors.
Autonomous Vehicles
Bird's-Eye View Semantic Segmentation
+1
Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories
In this paper, we revisit Sand and Teller's "particle video" approach, and study pixel tracking as a long-range motion estimation problem, where every pixel is described with a trajectory that locates it in multiple future frames.
CoCoNets: Continuous Contrastive 3D Scene Representations
This paper explores self-supervised learning of amodal 3D feature representations from RGB and RGB-D posed images and videos, agnostic to object and scene semantic content, and evaluates the resulting scene representations in the downstream tasks of visual correspondence, object tracking, and object detection.
Track, Check, Repeat: An EM Approach to Unsupervised Tracking
We propose an unsupervised method for detecting and tracking moving objects in 3D, in unlabelled RGB-D videos.
Move to See Better: Self-Improving Embodied Object Detection
Experiments on both indoor and outdoor datasets show that (1) our method obtains high-quality 2D and 3D pseudo-labels from multi-view RGB-D data; (2) fine-tuning with these pseudo-labels improves the 2D detector significantly in the test environment; (3) training a 3D detector with our pseudo-labels outperforms a prior self-supervised method by a large margin; (4) given weak supervision, our method can generate better pseudo-labels for novel objects.
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations
We can compare the 3D feature maps of two objects by searching alignment across scales and 3D rotations, and, as a result of the operation, we can estimate pose and scale changes without the need for 3D pose annotations.
Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping
We propose to leverage multiview data of \textit{static points} in arbitrary scenes (static or dynamic), to learn a neural 3D mapping module which produces features that are correspondable across time.
Embodied Language Grounding with 3D Visual Feature Representations
We propose associating language utterances to 3D visual abstractions of the scene they describe.
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping
Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction.
Image Disentanglement and Uncooperative Re-Entanglement for High-Fidelity Image-to-Image Translation
Cross-domain image-to-image translation should satisfy two requirements: (1) preserve the information that is common to both domains, and (2) generate convincing images covering variations that appear in the target domain.
Reward Learning from Narrated Demonstrations
Humans effortlessly "program" one another by communicating goals and desires in natural language.
Segmentation-Aware Convolutional Networks Using Local Attention Masks
We introduce an approach to integrate segmentation information within a convolutional neural network (CNN).
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision
Researchers have developed excellent feed-forward models that learn to map images to desired outputs, such as to the images' latent factors, or to other images, using supervised learning.
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness
Recently, convolutional networks (convnets) have proven useful for predicting optical flow.
Learning Dense Convolutional Embeddings for Semantic Segmentation
That is, for any two pixels on the same object, the embeddings are trained to be similar; for any pair that straddles an object boundary, the embeddings are trained to be dissimilar.
Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval
This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs).
