Search Results for author:
Found 26 papers, 15 papers with code
MVTN: Learning Multi-View Transformations for 3D Understanding
Multi-view projection techniques have shown themselves to be highly effective in achieving top-performing results in the recognition of 3D shapes.
Localizing Objects in 3D from Egocentric Videos with Visual Queries
With the recent advances in video and 3D understanding, novel 4D spatio-temporal challenges fusing both concepts have emerged.
SegNeRF: 3D Part Segmentation with Neural Radiance Fields
The predicted semantic fields allow SegNeRF to achieve an average mIoU of $\textbf{30. 30%}$ for 2D novel view segmentation, and $\textbf{37. 46%}$ for 3D part segmentation, boasting competitive performance against point-based methods by using only a few posed images.
Estimating more camera poses for ego-centric videos is essential for VQ3D
Visual queries 3D localization (VQ3D) is a task in the Ego4D Episodic Memory Benchmark.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos
Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation.
3DeformRS: Certifying Spatial Deformations on Point Clouds
In this work, we propose 3DeformRS, a method to certify the robustness of point cloud Deep Neural Networks (DNNs) against real-world deformations.
Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders
Hyperspectral imaging has attracted significant attention to identify spectral signatures for image classification and automated pattern recognition in computer vision.
MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions
The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques.
Ranked #1 on
Natural Language Moment Retrieval
on MAD
Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding
To this end, we introduce the concept of the multi-view point cloud (Voint cloud), representing each 3D point as a set of features extracted from several view-points.
SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation
We propose a novel scene flow estimation approach to capture and infer 3D motions from point clouds.
Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting
Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games.
Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts
In this paper, we focus our analysis on action spotting in soccer broadcast, which consists in temporally localizing the main actions in a soccer game.
Ranked #1 on
Action Spotting
on SoccerNet-v2
SALA: Soft Assignment Local Aggregation for Parameter Efficient 3D Semantic Segmentation
Since it is learnable, this mapping is allowed to be different per layer instead of being applied uniformly throughout the depth of the network.
MVTN: Multi-View Transformation Network for 3D Shape Recognition
MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.
Ranked #1 on
3D Object Retrieval
on ShapeNetCore 55
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production.
Ranked #1 on
Camera shot segmentation
on SoccerNet-v2
TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks
Extensive experiments show that using features trained with our novel pretraining strategy significantly improves the performance of recent state-of-the-art methods on three tasks: Temporal Action Localization, Action Proposal Generation, and Dense Video Captioning.
LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks
In this paper, we introduce a new NAS framework, dubbed LC-NAS, where we search for point cloud architectures that are constrained to a target latency.
A Context-Aware Loss Function for Action Spotting in Soccer Videos
We benchmark our loss on a large dataset of soccer videos, SoccerNet, and achieve an improvement of 12. 8% over the baseline.
Ranked #2 on
Action Spotting
on SoccerNet-v2
PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement
We integrate residual GCNs in a two-stage 3D object detection pipeline, where 3D object proposals are refined using a novel graph representation.
Ranked #14 on
3D Object Detection
on KITTI Cars Hard
Efficient Bird Eye View Proposals for 3D Siamese Tracking
Successively, we refine our selection of 3D object candidates by exploiting the similarity capability of a 3D Siamese network.
Leveraging Shape Completion for 3D Siamese Tracking
We design a Siamese tracker that encodes model and candidate shapes into a compact latent representation.
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
A total of 6, 637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution).
Ranked #6 on
Action Spotting
on SoccerNet
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild
In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild.
Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline
We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances.
A Solution for Crime Scene Reconstruction using Time-of-Flight Cameras
In this work, we propose a method for three-dimensional (3D) reconstruction of wide crime scene, based on a Simultaneous Localization and Mapping (SLAM) approach.
