Search Results for author:
Found 23 papers, 12 papers with code
Linking vision and motion for self-supervised object-centric perception
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features.
Reimagining an autonomous vehicle
The self driving challenge in 2021 is this century's technological equivalent of the space race, and is now entering the second major decade of development.
FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras
We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras.
Ranked #1 on
Bird's-Eye View Semantic Segmentation
on nuScenes
Atlas: End-to-End 3D Scene Reconstruction from Posed Images
Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene.
Ranked #1 on
3D Reconstruction
on ScanNet
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points
Cost volume based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the accuracy of MVS systems.
Ranked #1 on
Depth Estimation
on ScanNetV2
MagicEyes: A Large Scale Eye Gaze Estimation Dataset for Mixed Reality
With the emergence of Virtual and Mixed Reality (XR) devices, eye tracking has received significant attention in the computer vision community.
Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes
We introduce Scan2Plan, a novel approach for accurate estimation of a floorplan from a 3D scan of the structural elements of indoor environments.
EyeNet: A Multi-Task Network for Off-Axis Eye Gaze Estimation and User Understanding
Eye gaze estimation and simultaneous semantic understanding of a user through eye images is a crucial component in Virtual and Mixed Reality; enabling energy efficient rendering, multi-focal displays and effective interaction with 3D content.
DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences
We present DeepPerimeter, a deep learning based pipeline for inferring a full indoor perimeter (i. e. exterior boundary map) from a sequence of posed RGB images.
Gradient Adversarial Training of Neural Networks
We demonstrate gradient adversarial training for three different scenarios: (1) as a defense to adversarial examples we classify gradient tensors and tune them to be agnostic to the class of their corresponding example, (2) for knowledge distillation, we do binary classification of gradient tensors derived from the student or teacher network and tune the student gradient tensor to mimic the teacher's gradient tensor; and (3) for multi-task learning we classify the gradient tensors derived from different task loss functions and tune them to be statistically indistinguishable.
Estimating Depth from RGB and Sparse Sensing
We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels.
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly.
RoomNet: End-to-End Room Layout Estimation
This paper focuses on the task of room layout estimation from a monocular RGB image.
Deep Cuboid Detection: Beyond 2D Bounding Boxes
We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects).
Understanding Real World Indoor Scenes With Synthetic Data
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments.
SceneNet: Understanding Real World Indoor Scenes With Synthetic Data
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments.
TemplateNet for Depth-Based Object Instance Recognition
We present a novel deep architecture termed templateNet for depth based object instance recognition.
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making.
Symmetry-invariant optimization in deep networks
Recent works have highlighted scale invariance or symmetry that is present in the weight space of a typical deep network and the adverse effect that it has on the Euclidean gradient based stochastic gradient descent optimization.
Understanding symmetries in deep networks
Consequently, training the network boils down to using stochastic gradient descent updates on the unit-norm manifold.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Ranked #3 on
Medical Image Segmentation
on RITE
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling
These methods lack a mechanism to map deep layer feature maps to input dimensions.
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes
We are interested in automatic scene understanding from geometric cues.
