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Found 14 papers, 6 papers with code
DiST-4D: Disentangled Spatiotemporal Diffusion with Metric Depth for 4D Driving Scene Generation
To address this, we propose DiST-4D, the first disentangled spatiotemporal diffusion framework for 4D driving scene generation, which leverages metric depth as the core geometric representation.
MuDG: Taming Multi-modal Diffusion with Gaussian Splatting for Urban Scene Reconstruction
Recent breakthroughs in radiance fields have significantly advanced 3D scene reconstruction and novel view synthesis (NVS) in autonomous driving.
HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation
Driving World Models (DWMs) have become essential for autonomous driving by enabling future scene prediction.
UniScene: Unified Occupancy-centric Driving Scene Generation
UniScene employs a progressive generation process that decomposes the complex task of scene generation into two hierarchical steps: (a) first generating semantic occupancy from a customized scene layout as a meta scene representation rich in both semantic and geometric information, and then (b) conditioned on occupancy, generating video and LiDAR data, respectively, with two novel transfer strategies of Gaussian-based Joint Rendering and Prior-guided Sparse Modeling.
M${^2}$Depth: Self-supervised Two-Frame Multi-camera Metric Depth Estimation
This paper presents a novel self-supervised two-frame multi-camera metric depth estimation network, termed M${^2}$Depth, which is designed to predict reliable scale-aware surrounding depth in autonomous driving.
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction
This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023.
LayerDiffusion: Layered Controlled Image Editing with Diffusion Models
During the diffusion process, an iterative guidance strategy is used to generate a final image that aligns with the textual description.
Rethinking Dimensionality Reduction in Grid-based 3D Object Detection
In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically focus on the valuable parts of the object during voxel-to-BEV feature transformation.
KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo
Supervised multi-view stereo (MVS) methods have achieved remarkable progress in terms of reconstruction quality, but suffer from the challenge of collecting large-scale ground-truth depth.
Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives
Recently, Implicit Neural Representations (INRs) parameterized by neural networks have emerged as a powerful and promising tool to represent different kinds of signals due to its continuous, differentiable properties, showing superiorities to classical discretized representations.
Adaptive Assignment for Geometry Aware Local Feature Matching
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance.
Enhancing Multi-view Stereo with Contrastive Matching and Weighted Focal Loss
Learning-based multi-view stereo (MVS) methods have made impressive progress and surpassed traditional methods in recent years.
WT-MVSNet: Window-based Transformers for Multi-view Stereo
In this work, we propose Window-based Transformers (WT) for local feature matching and global feature aggregation in multi-view stereo.
TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers
We analogize MVS back to its nature of a feature matching task and therefore propose a powerful Feature Matching Transformer (FMT) to leverage intra- (self-) and inter- (cross-) attention to aggregate long-range context information within and across images.
Ranked #9 on
3D Reconstruction
on DTU
