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Found 25 papers, 12 papers with code
FreeSplat++: Generalizable 3D Gaussian Splatting for Efficient Indoor Scene Reconstruction
After the feed-forward reconstruction of 3DGS primitives, we investigate a depth-regularized per-scene fine-tuning process.
Unified Geometry and Color Compression Framework for Point Clouds via Generative Diffusion Priors
With the growth of 3D applications and the rapid increase in sensor-collected 3D point cloud data, there is a rising demand for efficient compression algorithms.
Hyperbolic Binary Neural Network
Specifically, we transform the constrained problem in hyperbolic space into an unconstrained one in Euclidean space using the Riemannian exponential map.
GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency
Identifying affordance regions on 3D objects from semantic cues is essential for robotics and human-machine interaction.
Learning to Decouple the Lights for 3D Face Texture Modeling
According to experiments on both single images and video sequences, we demonstrate the effectiveness of our approach in modeling facial textures under challenging illumination affected by occlusions.
VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
Although 3D Gaussian Splatting has been widely studied because of its realistic and efficient novel-view synthesis, it is still challenging to extract a high-quality surface from the point-based representation.
FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenes
However, existing generalizable 3D Gaussian Splatting methods are largely confined to narrow-range interpolation between stereo images due to their heavy backbones, thus lacking the ability to accurately localize 3D Gaussian and support free-view synthesis across wide view range.
ComPC: Completing a 3D Point Cloud with 2D Diffusion Priors
3D point clouds directly collected from objects through sensors are often incomplete due to self-occlusion.
FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio
In this paper, we abstract the process of people hearing speech, extracting meaningful cues, and creating various dynamically audio-consistent talking faces, termed Listening and Imagining, into the task of high-fidelity diverse talking faces generation from a single audio.
Learnable Chamfer Distance for Point Cloud Reconstruction
As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point distance between point clouds matched with predefined rules.
MaxQ: Multi-Axis Query for N:M Sparsity Network
Meanwhile, a sparsity strategy that gradually increases the percentage of N:M weight blocks is applied, which allows the network to heal from the pruning-induced damage progressively.
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
This paper proposes a decentralized Riemannian conjugate gradient descent (DRCGD) method that aims at minimizing a global function over the Stiefel manifold.
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning
In this paper, we propose a data-free mixed-precision compensation (DF-MPC) method to recover the performance of an ultra-low precision quantized model without any data and fine-tuning process.
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion
However, how to effectively exploit the relationships between the semantic context in semantic segmentation and geometric structure in scene completion remains under exploration.
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator
More specifically, given a textured face as the source and the rendered face projected from the desired 3DMM coefficients as the target, our proposed Texture-Geometry-aware Diffusion Model decomposes the complex transfer problem into multi-conditional denoising process, where a Texture Attention-based module accurately models the correspondences between appearance and geometry cues contained in source and target conditions, and incorporate extra implicit information for high-fidelity talking face generation.
Rethinking Mobile Block for Efficient Attention-based Models
This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance.
Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space
Specifically, we use the contrastive constraint to help CALoss learn a representation space with shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths.
SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud.
Thoughts on the Consistency between Ricci Flow and Neural Network Behavior
During the training process of the neural network, we observe that its metric will also regularly converge to the linearly nearly Euclidean metric, which is consistent with the convergent behavior of linearly nearly Euclidean metrics under the Ricci-DeTurck flow.
Riemannian Manifold Embeddings for Straight-Through Estimator
Quantized Neural Networks (QNNs) aim at replacing full-precision weights $\boldsymbol{W}$ with quantized weights $\boldsymbol{\hat{W}}$, which make it possible to deploy large models to mobile and miniaturized devices easily.
Manifold Micro-Surgery with Linearly Nearly Euclidean Metrics
The Ricci flow is a method of manifold surgery, which can trim manifolds to more regular.
Up-to-Down Network: Fusing Multi-Scale Context for 3D Semantic Scene Completion
An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems.
Ranked #7 on
3D Semantic Scene Completion
on SemanticKITTI
Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds
Specifically, the network takes a raw point cloud as input, and merges the features from the segmentation branch into the completion branch hierarchically to provide semantic information.
Ranked #5 on
3D Semantic Scene Completion
on SemanticKITTI
SSC: Semantic Scan Context for Large-Scale Place Recognition
We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance.
Ranked #1 on
Visual Place Recognition
on KITTI
RFNet: Recurrent Forward Network for Dense Point Cloud Completion
The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline.
