NeurIPS 2026 论文列表

While a converged 3D Gaussian Splatting (3DGS) model may accurately approximate a target scene, its underlying parameterization often becomes severely ill-suited for further optimization. We identify this late-stage bottleneck as \emph{parameterization degeneration}: high-opacity floaters truncate gradient flow to background surfaces via alpha compositing, and redundant overlapping clusters cause severe parameter coupling with nearly collinear Jacobian responses. These structural barriers explain why continued optimization plateaus, even when removable artifacts persist. To break this deadlock, we propose ReorgGS, an equivalent distribution reorganization method. By treating the converged Gaussian set as an empirical probability field, ReorgGS resamples centers, estimates local anisotropic covariances via kNN, and initializes a low-opacity state before resuming optimization. Unlike standard opacity reset—which only rescales weights on a flawed topology—ReorgGS fundamentally rebuilds the spatial and visibility structure. Our analysis reveals a crucial insight: \emph{distributional equivalence does not imply optimization equivalence}. By preserving scene support while drastically improving gradient accessibility and reducing opacity-weighted overlap, ReorgGS provides a vastly superior optimization landscape. Under the same optimization budget and fixed Gaussian count, ReorgGS breaks the performance ceiling, suppresses persistent floaters, and reduces rendering overhead by eliminating redundant overlap.

We study compression of serialized 3D point transformers for efficient point cloud segmentation. Starting from LitePT-S, we derive \textbf{TrimPT}, a compact student that reduces channel width and stage-3 attention depth while preserving the full 1024-point attention window. TrimPT uses 5.84\,M parameters and 12.95\,GFLOPs, giving $2.18\times$ fewer parameters and $1.96\times$ fewer FLOPs than LitePT-S. To improve the compressed student, we introduce \textbf{Stage-wise Relational Feature Distillation (SRFD)}, a training-only objective that matches pairwise cosine-similarity matrices between teacher and student features at the compressed attention stages. This explicitly regularizes teacher--student affinity mismatch and adds no inference-time cost because the teacher and projection heads are discarded after training. On ScanNet semantic segmentation, the resulting \textbf{TopoPT} reaches 76.6\% mIoU with a 5.84\,M parameter inference footprint, improving over TrimPT without SRFD and matching the official LitePT-S result with substantially fewer inference-time parameters and FLOPs. TopoPT also obtains 63.9\% mAP$_{50}$ on ScanNet instance segmentation, 33.0\% mAP$_{50}$ on ScanNet200, and 81.4\% mIoU on nuScenes, suggesting that stage-wise relational distillation is a useful training-time regularizer for lightweight 3D segmentation backbones. Code and models are available at: \url{https://github.com/anon-push/TopoPT}

Recent advances in 3D reconstruction have progressed from per-scene optimization to feed-forward inference, and semantic scene understanding has followed suit—yet existing methods remain confined to object-centric perception, neglecting spatial relations between objects. We introduce \emph{3D spatial relation segmentation}, a task that requires models to identify a target object satisfying a given spatial relation with respect to a subject, consistently across multiple views. To this end, we propose RelationVGGT, a novel feed-forward framework that integrates semantic features from a visual foundation model with geometry-aware representations from a 3D geometry foundation model and leverages a relation transformer for subject-conditioned, cross-view relation prediction—requiring neither per-scene optimization nor known camera poses. We additionally provide a fully automated annotation pipeline built on ScanNet++ with VLMs and LLMs, enabling scalable training data generation for this new task.

Open-vocabulary 3D scene understanding provides an important interface for querying and interacting with reconstructed scenes through natural language. Although recent 3DGS methods have enabled text-driven object selection and open-vocabulary segmentation, they still struggle with compositional queries involving attributes, spatial relations, and part-whole relations. Most existing approaches learn per-Gaussian or per-cluster language features, construct query-conditioned referring fields, or perform spatial reasoning only at inference time, but they do not organize the scene itself into a reusable relation-aware representation. As a result, object relations are not explicitly stored as typed and confidence-aware 3D structures, limiting compositional reasoning and multi-hop querying. To address this issue, we propose RelGS, a relation-aware 3D Gaussian Splatting framework for open-vocabulary scene understanding. RelGS learns semantic and instance embeddings for Gaussians under multi-view supervision, groups them into cross-view consistent 3D semantic nodes, and enriches each node with structured attributes verified by reverse CLIP consistency. It further constructs a confidence-weighted relation graph, where 3D spatial cues propose candidate relations and an LLM verifies their semantic plausibility. At query time, RelGS combines attribute matching, CLIP retrieval, and graph traversal to localize the target object. Extensive experiments demonstrate strong performance on word-level selection, sentence-level referring segmentation, multi-hop relational queries, and point-level semantic segmentation.

3D Gaussian Splatting enables real-time novel view synthesis with high fidelity, yet its sparse and unorganized Gaussian anchors make compression challenging without harming structure and cross-view consistency. A key challenge is that effective compression depends not only on reducing parameter redundancy, but also on identifying where the compressed model is most fragile and how limited representation capacity should be allocated accordingly. We present an RL-guided distillation framework for 3DGS compression (RLGS). RLGS first uses a lightweight reinforcement learning policy to predict informative viewpoint offsets for adaptive distillation. It then distills a compact student 3DGS from a high-quality teacher under multi-view rendering supervision. Finally, it introduces an Unbalanced Optimal Transport based structural selection module to align teacher-student anchor distributions in voxelized space, thereby retaining the most informative anchors during pruning and allocation. Under the same bitrate constraints, our method consistently outperforms strong 3DGS compression baselines in rendering quality. At matched rendering quality, it further reduces model size while preserving structural fidelity, achieving up to about 15% additional size reduction at comparable rendering quality over prior methods.

We present Register Anything Model (RAM), a unified model for generalizable 3D point cloud registration that robustly estimates 6-DoF transformations across diverse domains spanning sensors and environments. Existing registration methods are typically tailored to a single domain, with carefully tuned architectures and hyperparameters, and thus degrade markedly when applied to novel domains. We bridge this gap by training a single model on diverse data from indoor, outdoor, and object domains. A central challenge is the severe geometric discrepancy across domains, i.e., differences in scene scale, sensor noise, point density, and structural patterns, which hinders processing with a unified point-based model. To address this, we introduce a geometry-aware normalization strategy that jointly voxelizes and normalizes point clouds into a standarized geometric space, adaptively based on their geometric properties to preserve salient local structures while standardizing resolution. Built on this normalization, RAM follows a hierarchical coarse-to-fine paradigm, and employs an efficient geometric transformer that models local geometric consistency instead of over all points. This design mitigates discretization noise introduced by voxelization, and improves computational efficiency. Extensive experiments on $12$ benchmarks demonstrate state-of-the-art performance in terms of inlier ratio and registration recall. Notably, our method outperforms the counterparts trained on each separate domain, showing superior generalization capability. Code and models will be released upon publication.

Recovering 4D human-object interaction (HOI) from monocular video is a key step toward scalable 3D content creation, embodied AI, and simulation-based learning. Recent methods can reconstruct temporally coherent human and object trajectories, but these trajectories often remain visual artifacts while failing to preserve stable contact, functional manipulation, or physical plausibility when used as reference motions for humanoid-object simulation. This reveals a fundamental interaction gap: HOI reconstruction should not stop at tracking a human and an object, but should recover the relation that makes their motion a coherent interaction. We introduce **HA-HOI**, a framework for reconstructing physically plausible 4D HOI animation from in-the-wild monocular videos. Instead of treating the human and object as independent entities in an ambiguous monocular 3D space, we propose a \emph{human-first, object-follow} formulation. The human motion is recovered as the interaction anchor, and the object is reconstructed, aligned, and refined relative to the human action. The resulting kinematic trajectory is then projected into a physics-based humanoid-object simulation, where it acts as a teacher trajectory for stable physical rollout. Across benchmark and in-the-wild videos, **HA-HOI** improves human-object alignment, contact consistency, temporal stability, and simulation readiness over prior monocular HOI reconstruction methods. By moving beyond visually plausible trajectory recovery toward physically grounded interaction animation, our work takes a step toward turning general monocular HOI videos into scalable demonstrations for humanoid-object behavior.

Recent 3D foundation models, such as DUSt3R, MASt3R, VGGT, $\pi^3$, and Depth Anything 3, provide strong feed-forward depth and pose estimates on pinhole imagery, but degrade sharply under fisheye camera geometry. We show that this failure is partly caused by a pinhole camera bias in the positional encodings of pretrained 3D foundation models, and propose RayTun3R, a lightweight camera adaptation approach. It keeps the pretrained network fixed and adapts only lightweight components tied to token position and camera geometry. RayTun3R learns parameter-efficient residual corrections to absolute and rotary positional encodings, together with parameter-free tokenization and corrections to prediction-grid coordinates that remove residual pinhole assumptions. The resulting adapter contains only 10,752 trainable parameters and can be learned from a short temporal segment using geometric losses. Once adapted, RayTun3R transfers effectively to the remaining frames of the sequence without incurring additional runtime costs. Across diverse fisheye datasets with fields of view from $110^\circ$ to $200^\circ$, our adapter reduces rotation error by $2-12\times$ relative to the unadapted model, outperforms LoRA while using $\sim14\times$ fewer trainable parameters, improves pose over adaptation-free baselines while avoiding their multi-view inference cost, and remains competitive on depth accuracy.

Point cloud registration remains challenging in scenarios with low overlap, repetitive structures, and local geometric ambiguities, where candidate correspondences established based on local geometric similarity often lack global structural consistency and are prone to unreliable matches. To address this issue, this paper proposes RSPReg, a reliability-aware structural prototype learning framework for robust point cloud registration. RSPReg learns structural prototypes from rotation-invariant coarse-level superpoint features to characterize the structural attributes of local regions within the entire point cloud. These prototypes are further introduced as structural priors into cross-cloud correspondence reasoning, thereby improving the structural consistency of candidate correspondences. Furthermore, RSPReg estimates the uncertainty of structural prototypes and adaptively modulates the role of structural priors in the matching and pose estimation processes, reducing the negative impact of ambiguous and non-overlapping regions on registration results. Experiments on 3DMatch, 3DLoMatch, KITTI, and ETH demonstrate that RSPReg achieves the highest registration recall and exhibits clear advantages in low-overlap and cross-domain scenarios.

Full-dataset NeRF evaluation can require training and evaluating thousands of scenes, making exhaustive comparison expensive even when the benchmark is fixed. We introduce RASS, a risk-audited protocol for constructing compact NeRF benchmark subsets with explicit fidelity criteria. Given full-dataset per-scene logs, RASS repeatedly samples candidate subsets, checks whether their global metric means and score distributions match the full benchmark within user-defined tolerances, and recommends the smallest evaluated subset size whose Wilson lower confidence bound exceeds a target pass probability. We instantiate RASS on 3,522 Nutrition5k-derived food scenes using Zip-NeRF logs for the formal audit and PSNR, SSIM, LPIPS, and Kolmogorov-Smirnov (KS) distributional tests as fidelity criteria. The released 48-scene balanced operating point, RASS-48, is the lowest-cost evaluated screening subset that passes our declared global-fidelity audit and is intended for low-cost early ablations and rapid screening. A stricter target pass probability, $p_{\min}=0.20$, selects RASS-96, which we recommend as the compact reporting subset: it also passes joint fidelity audits over three and four view-synthesis methods. We also evaluate alternative subset generators, including uniform random sampling, farthest-first selection, and facility-location diversity sampling, and find that the RASS audit can validate different generators under the same fidelity contract. On DL3DV, a re-declared contract certifies 64 of 140 scenes for four methods under proportional allocation. We release the RASS-48 and RASS-96 scenes, configurations, descriptors, regime labels, seeds, audit scripts, and permitted logs to enable reproducible, compact NeRF evaluation. Project page: https://amughrabi.github.io/RASS/.

Recent advances in sparse voxel-based VAEs have demonstrated remarkable capabilities in high-fidelity 3D autoencoding, yet their generative counterparts consistently lag behind their reconstruction performance. A fundamental cause of this gap lies in the \emph{brittle output representation} in sparse voxel decoders, which parameterize geometry through discrete topological decisions, such as intersection flags or occupancy signs. Small perturbations in these parameters, which are unavoidable under diffusion sampling, can be amplified into abrupt topological ruptures and grid-like artifacts. We argue that a generation-friendly representation should ensure that such perturbations induce only smooth geometric transitions. To this end, we propose Pygmalion, a generation-friendly autoencoding framework that reformulates sparse voxel decoding as a coupled SDF parameterization. To retain explicit mesh-level supervision within this parameterization, which is non-trivial as the SDF jointly governs both topology and geometry, we introduce hinge-based sign correction, case-aware geometry supervision, and rendering-based refinement, achieving high-fidelity reconstruction while preserving generative robustness. Built upon this generation-friendly representation, Pygmalion scales consistently across DiT model sizes and voxel resolutions. Extensive experiments demonstrate state-of-the-art reconstruction fidelity and high-quality 3D generation with complete surfaces, sharp features, and fine geometric details.

Understanding reflection remains a long-standing challenge in 3D reconstruction due to the entanglement of appearance and geometry under view-dependent reflections. In this work, we present the Pygmalion Effect in Vision, a novel framework that metaphorically “sculpts” reflective objects into clay-like forms through image-to-clay translation. We then introduce a dual-branch design in which a BRDF-based reflective branch and a clay-guided branch share the same Gaussian geometry but operate through independent rendering paths. The clay-guided branch, supervised by the synthesized clay images, provides reflection-free geometric guidance that complements the photometric supervision of the BRDF branch. Experiments on both synthetic and real datasets show consistent improvements in both geometry and photometric metrics. Beyond technical gains, our framework reveals that seeing by unshining, translating radiance into neutrality, can serve as a powerful inductive bias for reflective object geometry learning.

3D Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis, but practical scenes often contain millions of Gaussians, making compression essential for deployment on limited hardware. Existing reduction methods are effective but mostly heuristic: they provide no multiplicative approximation guarantee for the rendered objective, and thus rely heavily on costly post-pruning finetuning to recover quality. We ask a basic question: can a 3DGS scene be provably replaced by a much smaller weighted subset (coreset) while preserving the objective of interest? We first show that, in the unrestricted setting, no non-trivial multiplicative 3DGS coreset exists. We then show that multiplicative guarantees are not impossible, but resolution-dependent. For a prescribed rendering resolution, such as representative views or grids of views/rays, we provide the first weighted coreset construction theorem for 3DGS. The construction samples Gaussians by sensitivity: provable importance scores measuring each Gaussian’s role in the full-scene objective. Finally, under explicit validity and log-transmittance stability assumptions, we turn this objective guarantee into a rendering guarantee. Empirically, our method is strongest where deployment needs it most: aggressive compression with no or minimal recovery compute. In prune-only and very short finetuning regimes, it achieves state-of-the-art performance, showing that principled importance estimation can be both theoretically meaningful and practically useful.

Vision Transformers (ViTs) demonstrate strong performance, but their substantial memory footprint and computational overhead necessitate efficient compression techniques such as post-training quantization (PTQ). However, pushing ViTs to low-bit precision incurs significant accuracy degradation. Recent PTQ methods adopt block reconstruction-based optimization with curvature-aware objectives, typically implemented using Fisher-based approximations. These approaches rely on explicit curvature modeling based on diagonal or structured approximations, which still fail to capture cross-dimensional interactions. Moreover, they often involve matrix inversion, leading to numerical instability under ill-conditioned settings. To address these limitations, we propose Gradient-Projected Fisher Approximation for Quantization (GPFA-Q), a block reconstruction-based PTQ framework that avoids explicit curvature matrix construction while capturing off-diagonal interactions. First, we introduce Gradient-Projected Reconstruction (GPR), a reconstruction objective that captures cross-dimensional interactions without explicitly constructing curvature matrices. To further support GPR, we integrate Soft Grid Rounding (SGR), which reduces the mismatch between continuous reconstruction and discrete inference. Extensive experiments demonstrate that our GPFA-Q achieves the state-of-the-art performance in low-bit quantization across diverse vision tasks.

Recent multi-view Transformers (e.g., DUSt3R and VGGT) have advanced 3D reconstruction by scaling training data and model capacity from two views to many views and long sequences, subsuming the classical SfM/SLAM pipeline of feature extraction, matching, and pose estimation in a single feedforward pass. Yet this subsumption hides the interfaces between pipeline stages---correspondences, geometry, and pose are entangled in a single forward pass, **leaving supervised fine-tuning as the major path** to improvement and offering no mechanism to correct predictions from geometric evidence at test time. We propose PosePlaner to close this gap by reviving the classical coupling between correspondences and pose via flow matching, treating pose refinement as a conditional generation problem that takes coarse geometric matches and initial pose as input condition and fuses the resulting pose hypotheses into a single refined estimate, providing a continuous, data-driven analogue of RANSAC voting. Our method is trained efficiently on two-view data, yet serves as a periodic refiner for streaming n-view pose estimators with no retraining. Empirically, it corrects failures of feedforward predictors, substantially improves correspondence-based solvers, and reduces accumulated drift in long streaming sequences.

3D scene inpainting aims to recover missing or occluded regions in edited 3D scenes, while ensuring geometric and textural consistency. Existing approaches, however, typically require accurately calibrated camera poses, which restricts their applicability in casual, in-the-wild scenarios and introduces additional preprocessing overhead. To overcome this limitation, we present FreeInpaint, a novel feed-forward framework that generates complete and 3D-consistent scenes directly from unposed multi-view images with masked regions. At its core, FreeInpaint extends a 3D foundation model to propagate masked regions from a reference view to other unposed views, bridging 3D reconstruction and scene inpainting while preserving the model's native ability to recover camera poses and scene geometry. Our method addresses two key challenges in adapting feed-forward 3D foundation models to masked inputs. First, masked regions can corrupt cross-view correspondence reasoning, degrading pose estimation and geometry recovery. To address this, we introduce a Learnable Mask Attention mechanism that preserves the spatial anchoring of reliable observations while allowing masked regions to progressively absorb useful context in deeper layers. Second, under severe occlusions, a single forward pass often lacks sufficient appearance evidence for high-fidelity completion. Therefore, we propose a Support Token Refinement strategy, which injects diffusion-generated support evidence as confidence-weighted auxiliary tokens to refine under-observed regions while preserving the original spatial anchor. Extensive experiments across diverse datasets demonstrate that FreeInpaint achieves superior inpainting quality, eliminating the reliance on pre-computed camera poses while keeping a fast inference speed.

Multimodal large language models have achieved remarkable progress, yet their language-dominant designs constrain visual perception and reasoning. Vision-centric multimodal large language models aim to restore visual understanding as the structural foundation, but existing systems rely on static visual expert fusion, leading to redundant computation, weak adaptivity, and limited multimodal transferability. Prior VC-MLLMs scale visual capacity by adding experts, whereas $\mathtt{PolyVision}$ scales visual capacity by allocating experts. We introduce $\mathtt{PolyVision}$, a vision-centric framework for conditional visual scaling in which dynamic visual expert routing assigns image patches to heterogeneous visual encoders. An \textbf{Attentive Router} selectively activates relevant experts for each patch, while a compact \textbf{VisPack} module executes routed representations efficiently and preserves example independence. Together with additional visual pre-alignment, this design turns heterogeneous visual scaling into a practical conditional-computation process. Built on the \texttt{Qwen3} backbone, $\mathtt{PolyVision}$ achieves superior efficiency and scalability on multimodal benchmarks. Experiments show that adaptive expert routing strengthens visual specialization, clarifies practical properties of visual scaling, and improves visual understanding at a favorable trade-off between efficiency and performance.

Rasterization dictates the interactive budget of 3D Gaussian Splatting (3DGS). However, the comparative speed of modern CUDA rasterizers is typically evaluated on a narrow canonical benchmark, masking severe regime-dependent performance reversals. This limited scope hides three critical kernel-level bottlenecks: warp lane underutilization, exposed global-memory latency during pixel shading, and terminal-tail penalties from hardware thread scheduling. In this paper, we present PolySplat, a workload-regime-aware 3DGS rasterizer designed to overcome these inefficiencies. PolySplat introduces a warp-saturating tile-key emitter with adaptive three-way dispatch, asynchronous shared-memory staging in the render kernel, and a persistent kernel architecture with centralized atomic dispatch to strictly bound tail latency. To rigorously validate our system, we introduce an extended 76-target benchmark that achieves comprehensive workload-regime coverage by stratifying targets across extreme Gaussian counts (up to 56.5M), high resolutions (up to 9K), and six diverse scene categories. Evaluated on this comprehensive suite, PolySplat achieves dataset-balanced geometric-mean speedups of 1.26x to 6.48x over state-of-the-art rasterizers at lossless visual quality. Notably, under sustained 60Hz interaction at extreme resolutions, PolySplat strictly meets 1-vsync display deadlines where existing renderers suffer from massive queue divergence and input-to-photon lag.

Backdoor attacks threaten the integrity of machine learning models by allowing attackers to control model behavior through triggers. Models can be compromised during finetuning by backdoor attacks through either poisoned data or adversarial training objectives. Because existing finetuning-activated attacks assume limited domain shift or frozen encoder layers, they often fail under full-model finetuning. We target a stealthy finetuning-activated attack where a dormant backdoor is implanted in a pretrained encoder, and later activated by finetuning on clean downstream data. We propose Poison-then-Hide, a novel attack that remains effective when the entire model is finetuned in a domain transfer. Our approach consists of three components: trigger optimization, base encoder poisoning, and targeted unlearning to conceal the backdoor. We evaluate our method on six datasets and three model architectures, and achieve state-of-the-art (up to 97%) attack success rates. We find that two design choices - jointly learning the benign target task and the backdoor during encoder poisoning, and optimizing the trigger for attack robustness using an ensemble of simulated finetuned models - are critical to the attack's success. We demonstrate that standard detection and mitigation defenses cannot fully remove the backdoor, which can reappear after benign finetuning.