Gauss What You Need: Compact Gaussian Splatting Across Scene Scales introduces TangoGS, a method that automatically selects the number of Gaussian primitives for 3D Gaussian Splatting by combining capture-derived model sizing with training-based adaptation. The approach first estimates a learning allowance based on the capture’s total pixels, then adjusts the number of Gaussians during training according to reconstruction quality. On standard benchmarks, TangoGS matches the best baseline’s PSNR while using 48% fewer Gaussians, and on larger captures it scales automatically to achieve the highest mean PSNR with 2.3× more Gaussians.
By Afif Boudaoud, Jiayi Liu, Alexandru Calotoiu, Torsten Hoefler
arXiv:2608.29426v1 Announce Type: cross
Abstract: Reliable semantic representations derived from city-scale 3D models are increasingly important for urban analysis, infrastructure monitoring, autonom...
By Alexander Rusnak, Sophia Kovalenko, Jingru Wang, Ismail Moudden, Xiru Wang, Fr\'ed\'eric Kaplan
M3GD introduces a multimodal representation that fuses pre‑trained 2D image and 3D LiDAR foundation models for robotic novel view synthesis, avoiding the need for a separate cross‑modal translator. By projecting LiDAR onto the image latent grid and injecting the resulting geometry‑aware packets via a lightweight residual adapter, the method enhances both RGB and depth synthesis on the GrandTour dataset compared to an image‑only baseline. Ablation studies confirm that pixel‑aligned LiDAR content drives the performance gains, and real‑world deployment on a ground robot demonstrates a tunable quality–cost trade‑off.
By Yang Zhou, Jiuhong Xiao, Shizhao Ye, Long Quang, Carlos Nieto-Granda, Giuseppe Loianno
arXiv:2609.23404v1 Announce Type: new
Abstract: Point cloud completion aims to infer a complete 3D shape from a partial point cloud and serves as a fundamental building block for downstream tasks suc...
By Shenghui Wu, Chen Wang, Yuan Feng, Guangshun Wei, Yuanfeng Zhou, Changjian Li
EliGSiR is a continual RGB‑D mapping method that extends 3D Gaussian Splatting to online settings by adaptively allocating optimization resources. It introduces Map‑Guided View Scheduling to filter redundant views, Load‑Adaptive Fidelity to adjust supervision resolution, and Targeted Geometry Growth to add geometric capacity only where needed. The approach is evaluated on Replica, TUM RGB‑D, ScanNet++, and real sensor sequences, achieving higher reconstruction quality and faster performance than several baselines.
By Bj\"orn Ellensohn, Elmar Rueckert, Christian Rauch
EliGSiR is a continual RGB‑D mapping system that extends Gaussian splatting to handle online, bounded‑compute scenarios. It introduces Map‑Guided View Scheduling to filter redundant views, Load‑Adaptive Fidelity to adjust supervision resolution, and Targeted Geometry Growth to add structure only where needed. Experiments on Replica, TUM RGB‑D, ScanNet++ and real sensor data show that EliGSiR outperforms baselines in reconstruction quality while efficiently using the available mapping budget.
arXiv:2607. 05522v1 Announce Type: cross Abstract: 3D Gaussian splatting (3DGS) is a strong representation for real-time novel-view synthesis, but its standard training pipeline relies on point estimates and hand-tuned heuristics, providing no native uncertainty or principled complexity control.
By Gaoxiang Jia, Vikram Appia, Junzhou Huang, Xinlei Wang
arXiv:2511.16030v3 Announce Type: replace
Abstract: 3D Gaussian Splatting (3DGS) enables efficient, high-fidelity novel view synthesis, yet its performance degrades severely under sparse-view supervi...
By Zijian Wu, Mingfeng Jiang, Zidian Lin, Ying Song, Ziqian Lu, Qun Wu, Hanjie Ma
M3GD is a novel approach for robotic novel view synthesis that fuses camera images and LiDAR point clouds without requiring a separate cross‑modal translator. By projecting LiDAR data onto the image latent grid and injecting it via a lightweight residual adapter, M3GD enhances both RGB and depth generation on the GrandTour dataset compared to image‑only baselines. Experiments on a ground robot confirm that the method can be deployed in real‑world scenarios with a tunable quality‑cost trade‑off.
arXiv:2609.21347v1 Announce Type: new
Abstract: Recent progress in 3D Gaussian Splatting (3DGS) has enabled dense visual SLAM with pinhole cameras, yet most pipelines are not designed for panoramic i...
By Xiangfei Guo, Hao Shi, Yufan Zhang, Zhonghua Yi, Yongqi Mao, Xiaoting Yin, Kaiwei Wang
arXiv:2609.23182v1 Announce Type: new
Abstract: Feed-forward 3D Gaussian Splatting now reconstructs renderable scenes from unposed, uncalibrated images. Yet, most models supervise only photometric co...
By Si-Yu Lu, Yung-Yao Chen, Yi Jan Chen, Shang-Lin Li, Ching-Chan Liao, Wen-Huang Cheng
We present TRACE-GS, an on-policy trajectory distillation framework that leverages privileged geometric conditioning at training time, thereby adapting a diffusion prior to sparse-view 3D Gaussian Splatting (3DGS) restoration. Rather than pursuing increasingly sophisticated restoration architectures, we identify a more fundamental limitation shared by existing diffusion-based approaches: supervision at independently noised states does not cover those reached during inference.