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Found 56 papers, 10 papers with code
Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Video
Creating high-quality and interactive virtual environments, such as games and simulators, often involves complex and costly manual modeling processes.
MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based Monocular Guidance
Our experiments show 4x the performance of RegNeRF and 8x that of FreeNeRF on average F1@2cm for ETH3D MVS benchmark, suggesting a fruitful research direction to improve the geometric accuracy of NeRF-based models, and sheds light on a potential future approach to enable NeRF-based optimization to eventually outperform traditional MVS.
GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh
We introduce GoMAvatar, a novel approach for real-time, memory-efficient, high-quality animatable human modeling.
Physical Property Understanding from Language-Embedded Feature Fields
Can computers perceive the physical properties of objects solely through vision?
LidarDM: Generative LiDAR Simulation in a Generated World
We present LidarDM, a novel LiDAR generative model capable of producing realistic, layout-aware, physically plausible, and temporally coherent LiDAR videos.
RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation
Robots need to explore their surroundings to adapt to and tackle tasks in unknown environments.
IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images
While numerous 3D reconstruction and novel-view synthesis methods allow for photorealistic rendering of a scene from multi-view images easily captured with consumer cameras, they bake illumination in their representations and fall short of supporting advanced applications like material editing, relighting, and virtual object insertion.
Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting
Reconstructing an object from photos and placing it virtually in a new environment goes beyond the standard novel view synthesis task as the appearance of the object has to not only adapt to the novel viewpoint but also to the new lighting conditions and yet evaluations of inverse rendering methods rely on novel view synthesis data or simplistic synthetic datasets for quantitative analysis.
Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects
Our world is full of identical objects (\emphe. g., cans of coke, cars of same model).
On the Overconfidence Problem in Semantic 3D Mapping
Semantic 3D mapping, the process of fusing depth and image segmentation information between multiple views to build 3D maps annotated with object classes in real-time, is a recent topic of interest.
ContactGen: Generative Contact Modeling for Grasp Generation
This paper presents a novel object-centric contact representation ContactGen for hand-object interaction.
MapPrior: Bird's-Eye View Map Layout Estimation with Generative Models
Despite tremendous advancements in bird's-eye view (BEV) perception, existing models fall short in generating realistic and coherent semantic map layouts, and they fail to account for uncertainties arising from partial sensor information (such as occlusion or limited coverage).
UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video
UrbanIR uses a novel loss to make very good estimates of shadow volumes in the original scene.
Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Clouds
We build rearticulable models for arbitrary everyday man-made objects containing an arbitrary number of parts that are connected together in arbitrary ways via 1 degree-of-freedom joints.
PEANUT: Predicting and Navigating to Unseen Targets
In this work, we present a straightforward method for learning these regularities by predicting the locations of unobserved objects from incomplete semantic maps.
QFF: Quantized Fourier Features for Neural Field Representations
Instead, we propose to encode features in bins of Fourier features that are commonly used for positional encoding.
ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field
Physical simulations produce excellent predictions of weather effects.
CASA: Category-agnostic Skeletal Animal Reconstruction
Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge.
Learning to Generate Realistic LiDAR Point Clouds
We present LiDARGen, a novel, effective, and controllable generative model that produces realistic LiDAR point cloud sensory readings.
Virtual Correspondence: Humans as a Cue for Extreme-View Geometry
Similar to classic correspondences, VCs conform with epipolar geometry; unlike classic correspondences, VCs do not need to be co-visible across views.
NeurMiPs: Neural Mixture of Planar Experts for View Synthesis
We present Neural Mixtures of Planar Experts (NeurMiPs), a novel planar-based scene representation for modeling geometry and appearance.
Deep Feedback Inverse Problem Solver
Specifically, at each iteration, the neural network takes the feedback as input and outputs an update on the current estimation.
Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild
Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and graphics.
Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving
In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV).
Deep Parametric Continuous Convolutional Neural Networks
Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks.
Ranked #2 on
Semantic Segmentation
on S3DIS Area5
(Number of params metric)
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation.
Asynchronous Multi-View SLAM
Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice.
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving.
SceneGen: Learning to Generate Realistic Traffic Scenes
Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones.
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles.
Convolutional Recurrent Network for Road Boundary Extraction
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely.
Deep Continuous Fusion for Multi-Sensor 3D Object Detection
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization.
Learning to Localize Using a LiDAR Intensity Map
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars.
Learning to Localize Through Compressed Binary Maps
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps.
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to reduce the bitrate of both geometry and intensity values.
DSDNet: Deep Structured self-Driving Network
In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network.
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation, producing realistic LiDAR point clouds.
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds.
Identifying Unknown Instances for Autonomous Driving
In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning.
Efficient Graph Generation with Graph Recurrent Attention Networks
Our model generates graphs one block of nodes and associated edges at a time.
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference.
DSIC: Deep Stereo Image Compression
In this paper we tackle the problem of stereo image compression, and leverage the fact that the two images have overlapping fields of view to further compress the representations.
Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization
In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters.
Deep Multi-Sensor Lane Detection
Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving.
Deep Rigid Instance Scene Flow
In this paper we tackle the problem of scene flow estimation in the context of self-driving.
Proximal Deep Structured Models
Many problems in real-world applications involve predicting continuous-valued random variables that are statistically related.
TorontoCity: Seeing the World with a Million Eyes
In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712. 5 $km^2$ of land, 8439 $km$ of road and around 400, 000 buildings.
AutoScaler: Scale-Attention Networks for Visual Correspondence
We propose AutoScaler, a scale-attention network to explicitly optimize this trade-off in visual correspondence tasks.
Find your Way by Observing the Sun and Other Semantic Cues
In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world.
The Global Patch Collider
In contrast to conventional approaches that rely on pairwise distance computation, our algorithm isolates distinctive pixel pairs that hit the same leaf during traversal through multiple learned tree structures.
HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images
In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes.
Enhancing Road Maps by Parsing Aerial Images Around the World
In recent years, contextual models that exploit maps have been shown to be very effective for many recognition and localization tasks.
Lost Shopping! Monocular Localization in Large Indoor Spaces
In this paper we propose a novel approach to localization in very large indoor spaces (i. e., 200+ store shopping malls) that takes a single image and a floor plan of the environment as input.
Holistic 3D Scene Understanding From a Single Geo-Tagged Image
In this paper we are interested in exploiting geographic priors to help outdoor scene understanding.
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials
In this paper, we prove that every multivariate polynomial with even degree can be decomposed into a sum of convex and concave polynomials.
